Methylation unsupervised EDA
Chloe Yap
01 February 2021
To run: Beforehand:
module load pandocIn R:
setwd("~/shared-gandalm/brain_CTP/Scripts/methylation/analysis/unsupervised")
rmarkdown::render("methylation_unsupervised_eda.Rmd", "html_document")1 Jaffe et al. 2018
1.1 Set-up
1.1.1 Directories and libraries
# QC-ed data (meffil pipeline)
eigenval_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_aut_mask.eigenval"
eigenvec_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_aut_mask.eigenvec"
eigenval_sd02_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_sd02_aut_mask.eigenval"
eigenvec_sd02_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_sd02_aut_mask.eigenvec"
eigenval_sd02_age18_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask.eigenval"
eigenvec_sd02_age18_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask.eigenvec"
eigenval_sd02_fetal_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_fetal_sd02_aut_mask.eigenval"
eigenvec_sd02_fetal_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_fetal_sd02_aut_mask.eigenvec"
pheno_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed/pheno_Jaffe2018.txt"
# Also check PCA calculated using Jaffe et al. 2018 processed data
eigenval_jaffe_sd02_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep_sd02_aut_mask.eigenval"
eigenvec_jaffe_sd02_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep_sd02_aut_mask.eigenvec"
# Jaffe CTP
ctp_jaffe_dir <- "~/shared-gandalm/GenomicDatasets/Jaffe_methylation_DLFPC/pheno/GSE74193_series_matrix_pheno_df.txt"
# ReFACTor components
refactor_k8_age18_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov.refactor.components.txt"
refactor_k8_age18_probe_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov.refactor.rankedlist.txt"
refactor_k8_age18ctrl_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov_controls.refactor.components.txt"
refactor_k8_age18ctrl_probe_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov_controls.refactor.rankedlist.txt"
refactor_k8_fetal_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_fetal_sd02_aut_mask_cov.refactor.components.txt"
refactor_k8_fetal_probe_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_fetal_sd02_aut_mask_cov.refactor.rankedlist.txt"
ilmn450k.gr_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/reference/HM450.hg19.manifest.rds"
refactor_k8_age18_n500.ma_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov.refactor.rankedlist_500_t.ma"
refactor_k8_age18ctrl_n500.ma_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_cov_controls.refactor.rankedlist_500_t.ma"
refactor_k8_fetal_n500.ma_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_fetal_sd02_aut_mask_cov.refactor.rankedlist_500_t.ma"
# Houseman results
hm_na2_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_Luo2020_na2.houseman_estimates.txt"
hm_na5_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/analysis/Jaffe2018_age18_sd02_aut_mask_Luo2020_na5.houseman_estimates.txt"
# proc_keep_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep.rds"
#proc_keep_eigenval_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep.eigenval"
#proc_keep_eigenvec_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep.eigenvec"
# proc_keep_filter_eigenval_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep_mask_sd02.eigenval"
# proc_keep_filter_eigenvec_dir <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep_mask_sd02.eigenvec"
# jaffe.keep_dir <- "/u/project/gandalm/shared/GenomicDatasets/Jaffe_methylation_DLFPC/pheno/GSE74193_series_matrix_pheno_keep_df.txt"
#mask_dir <- "/u/project/gandalm/shared/GenomicDatasets/Jaffe_methylation_DLFPC/EPIC.hg38.manifest_MASK_general.probe"
# pca_out <- "~/shared-gandalm/brain_CTP/Data/methylation/Jaffe2018/processed_Jaffe/GSE74193_GEO_procData_beta_keep_PC"
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# BiocManager::install("M3C")
# library(M3C)1.1.2 Read-in data and order
1.1.2.1 QC-ed data
eigenval <- fread(eigenval_dir)
eigenvec <- fread(eigenvec_dir)
eigenval_sd02 <- fread(eigenval_sd02_dir)
eigenvec_sd02 <- fread(eigenvec_sd02_dir)
eigenval_sd02_age18 <- fread(eigenval_sd02_age18_dir)
eigenvec_sd02_age18 <- fread(eigenvec_sd02_age18_dir)
eigenval_sd02_fetal <- fread(eigenval_sd02_fetal_dir)
eigenvec_sd02_fetal <- fread(eigenvec_sd02_fetal_dir)
colnames(eigenval) <- "eigenvalue"
colnames(eigenval_sd02) <- "eigenvalue"
colnames(eigenval_sd02_age18) <- "eigenvalue"
colnames(eigenval_sd02_fetal) <- "eigenvalue"
colnames(eigenvec) <- c("FID", "IID", paste(rep("PC", 10), 1:10, sep = ""))
colnames(eigenvec_sd02) <- c("FID", "IID", paste(rep("PC", 10), 1:10, sep = ""))
colnames(eigenvec_sd02_age18) <- c("FID", "IID", paste(rep("PC", 10), 1:20, sep = ""))
colnames(eigenvec_sd02_fetal) <- c("FID", "IID", paste(rep("PC", 10), 1:20, sep = ""))
pheno <- fread(pheno_dir)
ctp_jaffe <- fread(ctp_jaffe_dir)
ctp_jaffe <- ctp_jaffe[,c("title", "comp_da_neuron", "comp_es", "comp_neun_neg", "comp_neun_pos", "comp_npc")]
colnames(ctp_jaffe)[1] <- "IID"1.1.2.2 Jaffe et al. processed
eigenval_jaffe_sd02 <- read.delim(eigenval_jaffe_sd02_dir, header = F, as.is = T)
colnames(eigenval_jaffe_sd02) <- "eigenvalue"
eigenvec_jaffe_sd02 <- read.table(eigenvec_jaffe_sd02_dir, header = F, as.is = T)
colnames(eigenvec_jaffe_sd02) <- c("FID", "IID", paste(rep("PC", 10), 1:10, sep = ""))
#proc_keep_filter.eigenval <- read.delim(proc_keep_filter_eigenval_dir, header = F, as.is = T)
#colnames(proc_keep_filter.eigenval) <- "eigenvalue"
#proc_keep_filter.eigenvec <- read.table(proc_keep_filter_eigenvec_dir, header = F, as.is = T)
#colnames(proc_keep_filter.eigenvec) <- c("FID", "IID", paste(rep("PC", 10), 1:10, sep = ""))
#jaffe.keep <- read.delim(jaffe.keep_dir, header = T, as.is = T)
#colnames(jaffe.keep)[1] <- "IID"1.2 Check PCA concordance across filters and pipelines
1.2.1 Scree plots for % variance explained
- overall, looks like sd02 filter improves PCA
meffil pipeline, no sd02 filter
eigenval$variance_pc <- eigenval$eigenvalue/sum(eigenval$eigenvalue)
eigenval$variance_total <- cumsum(eigenval$variance_pc)
eigenval$PC <- 1:nrow(eigenval)
eigenval$filter <- "meffil"meffil pipeline, with sd02 filter
eigenval_sd02$variance_pc <- eigenval_sd02$eigenvalue/sum(eigenval_sd02$eigenvalue)
eigenval_sd02$variance_total <- cumsum(eigenval_sd02$variance_pc)
eigenval_sd02$PC <- 1:nrow(eigenval_sd02)
eigenval_sd02$filter <- "meffil_sd02"meffil pipeline, with sd02 filter, age>=18
eigenval_sd02_age18$variance_pc <- eigenval_sd02_age18$eigenvalue/sum(eigenval_sd02_age18$eigenvalue)
eigenval_sd02_age18$variance_total <- cumsum(eigenval_sd02_age18$variance_pc)
eigenval_sd02_age18$PC <- 1:nrow(eigenval_sd02_age18)
eigenval_sd02_age18$filter <- "meffil_sd02_age18"meffil pipeline, with sd02 filter, fetal
eigenval_sd02_fetal$variance_pc <- eigenval_sd02_fetal$eigenvalue/sum(eigenval_sd02_fetal$eigenvalue)
eigenval_sd02_fetal$variance_total <- cumsum(eigenval_sd02_fetal$variance_pc)
eigenval_sd02_fetal$PC <- 1:nrow(eigenval_sd02_fetal)
eigenval_sd02_fetal$filter <- "meffil_sd02_fetal"Jaffe pipeline, with sd02 filter
eigenval_jaffe_sd02$variance_pc <- eigenval_jaffe_sd02$eigenvalue/sum(eigenval_jaffe_sd02$eigenvalue)
eigenval_jaffe_sd02$variance_total <- cumsum(eigenval_jaffe_sd02$variance_pc)
eigenval_jaffe_sd02$PC <- 1:nrow(eigenval_jaffe_sd02)
eigenval_jaffe_sd02$filter <- "Jaffe_sd02"Graph of all
eigenval_agg <- rbind(eigenval, eigenval_sd02, eigenval_sd02_fetal, eigenval_sd02_fetal, eigenval_jaffe_sd02) %>% filter(PC %in% 1:25)
scree_agg <- ggplot(data=eigenval_agg, aes(x=PC, y=variance_pc)) +
geom_bar(stat="identity") + facet_wrap(~ filter, nrow = 3)
scree_agg
1.2.2 Correlations across first 10 PCs
- Plots compares results from 10 PCs from the meffilsd02 set, compared to:
- 10 PCs from Jaffe_sd02 filter
- 10 PCs from meffil without sd02 filter
- Overall
- PC1-4, 10: meffil_sd02 and Jaffe_sd02 look fairly cocordant
- PC1-2,4-5: meffil_sd02 and meffil look fairly concordant
- otherwise, looks like not so concordant
- note that when only looking at age > 10, there’s not much concordant, suggests that the PCs are picking up a LOT of age
eigenvec_sd02.long <- data.table::melt(eigenvec_sd02, id.vars = c("FID", "IID"), variable.name = "PC", value.name = "value_meffil_sd02")
eigenvec_sd02.long$filter_meffil_sd02 <- "meffil_sd02"
eigenvec_sd02_age18.long <- data.table::melt(eigenvec_sd02_age18, id.vars = c("FID", "IID"), variable.name = "PC")
eigenvec_sd02_age18.long$filter <- "meffil_sd02_age18"
eigenvec_sd02_fetal.long <- data.table::melt(eigenvec_sd02_fetal, id.vars = c("FID", "IID"), variable.name = "PC")
eigenvec_sd02_fetal.long$filter <- "meffil_sd02_fetal"
eigenvec_jaffe_sd02.long <- data.table::melt(eigenvec_jaffe_sd02, id.vars = c("FID", "IID"), variable.name = "PC")## Warning in data.table::melt(eigenvec_jaffe_sd02, id.vars = c("FID", "IID"), :
## The melt generic in data.table has been passed a data.frame and will attempt
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## deprecated, and this redirection is now deprecated as well. To continue using
## melt methods from reshape2 while both libraries are attached, e.g. melt.list,
## you can prepend the namespace like reshape2::melt(eigenvec_jaffe_sd02). In the
## next version, this warning will become an error.eigenvec_jaffe_sd02.long$filter <- "Jaffe_sd02"
# Join together
tmp1 <- join(eigenvec_sd02_age18.long, eigenvec_sd02.long, by = c("FID", "IID", "PC"), type = "inner")
tmp2 <- join(eigenvec_sd02_fetal.long, eigenvec_sd02.long, by = c("FID", "IID", "PC"), type = "inner")
tmp3 <- join(eigenvec_jaffe_sd02.long, eigenvec_sd02.long, by = c("FID", "IID", "PC"), type = "inner")
tmp4 <- rbind(tmp1, tmp2, tmp3)
ggplot(tmp4, aes(x=value, y=value_meffil_sd02)) +
geom_point() +
facet_grid(rows = vars(PC), cols = vars(filter))
1.3 Plot PCs
1.3.1 Add metadata first
eigenvec_sd02_pheno <- join(eigenvec_sd02, pheno, by = c("FID", "IID"), type = "inner")
eigenvec_sd02_age18_pheno <- join(eigenvec_sd02_age18, pheno, by = c("FID", "IID"), type = "inner")
eigenvec_sd02_fetal_pheno <- join(eigenvec_sd02_fetal, pheno, by = c("FID", "IID"), type = "inner")
eigenvec_jaffe_sd02_pheno <- join(eigenvec_jaffe_sd02, pheno, by = c("FID", "IID"), type = "inner")1.3.2 Check associations with each PC
- N.B. didn’t include slide as difficult to plot nicely
meffil with sd02 filter
eigenvec_sd02_pheno_PC12.group <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC12.sex <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC12.age <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC12.race <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC12.plate <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC13.group <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC13.sex <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC13.age <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC13.race <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC13.plate <- ggplot(eigenvec_sd02_pheno, aes(x=PC1, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC23.group <- ggplot(eigenvec_sd02_pheno, aes(x=PC2, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC23.sex <- ggplot(eigenvec_sd02_pheno, aes(x=PC2, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC23.age <- ggplot(eigenvec_sd02_pheno, aes(x=PC2, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC23.race <- ggplot(eigenvec_sd02_pheno, aes(x=PC2, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_pheno_PC23.plate <- ggplot(eigenvec_sd02_pheno, aes(x=PC2, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")meffil with sd02 filter, age >= 18
eigenvec_sd02_age18_pheno_PC12.group <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC12.sex <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC12.age <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC12.race <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC12.plate <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC13.group <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC13.sex <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC13.age <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC13.race <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC13.plate <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC1, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC23.group <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC2, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC23.sex <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC2, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC23.age <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC2, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC23.race <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC2, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_PC23.plate <- ggplot(eigenvec_sd02_age18_pheno, aes(x=PC2, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")meffil with sd02 filter, fetal
eigenvec_sd02_fetal_pheno_PC12.group <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC12.sex <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC12.age <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC12.race <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC12.plate <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC13.group <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC13.sex <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC13.age <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC13.race <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC13.plate <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC1, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC23.group <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC2, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC23.sex <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC2, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC23.age <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC2, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC23.race <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC2, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_PC23.plate <- ggplot(eigenvec_sd02_fetal_pheno, aes(x=PC2, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")Jaffe with sd02 filter
eigenvec_jaffe_sd02_pheno_PC12.group <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC12.sex <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC12.age <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC12.race <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC12.plate <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC13.group <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC13.sex <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC13.age <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC13.race <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC13.plate <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC1, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC23.group <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC2, y=PC3, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC23.sex <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC2, y=PC3, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC23.age <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC2, y=PC3, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC23.race <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC2, y=PC3, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_jaffe_sd02_pheno_PC23.plate <- ggplot(eigenvec_jaffe_sd02_pheno, aes(x=PC2, y=PC3, colour = plate)) + geom_point() + theme(legend.position = "bottom")1.3.3 Plots
1.3.3.1 PC1/PC2
ggarrange(eigenvec_sd02_pheno_PC12.group, eigenvec_sd02_age18_pheno_PC12.group, eigenvec_sd02_fetal_pheno_PC12.group, eigenvec_jaffe_sd02_pheno_PC12.group,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC12.sex, eigenvec_sd02_age18_pheno_PC12.sex, eigenvec_sd02_fetal_pheno_PC12.sex, eigenvec_jaffe_sd02_pheno_PC12.sex,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC12.age, eigenvec_sd02_age18_pheno_PC12.age, eigenvec_sd02_fetal_pheno_PC12.age, eigenvec_jaffe_sd02_pheno_PC12.age,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC12.race, eigenvec_sd02_age18_pheno_PC12.race, eigenvec_sd02_fetal_pheno_PC12.race, eigenvec_jaffe_sd02_pheno_PC12.race,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC12.plate, eigenvec_sd02_age18_pheno_PC12.plate, eigenvec_sd02_fetal_pheno_PC12.plate, eigenvec_jaffe_sd02_pheno_PC12.plate,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
1.3.3.2 PC1/PC3
ggarrange(eigenvec_sd02_pheno_PC13.group, eigenvec_sd02_age18_pheno_PC13.group, eigenvec_sd02_fetal_pheno_PC13.group, eigenvec_jaffe_sd02_pheno_PC13.group,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC13.sex, eigenvec_sd02_age18_pheno_PC13.sex, eigenvec_sd02_fetal_pheno_PC13.sex, eigenvec_jaffe_sd02_pheno_PC13.sex,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC13.age, eigenvec_sd02_age18_pheno_PC13.age, eigenvec_sd02_fetal_pheno_PC13.age, eigenvec_jaffe_sd02_pheno_PC13.age,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC13.race, eigenvec_sd02_age18_pheno_PC13.race, eigenvec_sd02_fetal_pheno_PC13.race, eigenvec_jaffe_sd02_pheno_PC13.race,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC13.plate, eigenvec_sd02_age18_pheno_PC13.plate, eigenvec_sd02_fetal_pheno_PC13.plate, eigenvec_jaffe_sd02_pheno_PC13.plate,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
1.3.3.3 PC2/PC3
ggarrange(eigenvec_sd02_pheno_PC23.group, eigenvec_sd02_age18_pheno_PC23.group, eigenvec_sd02_fetal_pheno_PC23.group, eigenvec_jaffe_sd02_pheno_PC23.group,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC23.sex, eigenvec_sd02_age18_pheno_PC23.sex, eigenvec_sd02_fetal_pheno_PC23.sex, eigenvec_jaffe_sd02_pheno_PC23.sex,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC23.age, eigenvec_sd02_age18_pheno_PC23.age, eigenvec_sd02_fetal_pheno_PC23.age, eigenvec_jaffe_sd02_pheno_PC23.age,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC23.race, eigenvec_sd02_age18_pheno_PC23.race, eigenvec_sd02_fetal_pheno_PC23.race, eigenvec_jaffe_sd02_pheno_PC23.race,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
ggarrange(eigenvec_sd02_pheno_PC23.plate, eigenvec_sd02_age18_pheno_PC23.plate, eigenvec_sd02_fetal_pheno_PC23.plate, eigenvec_jaffe_sd02_pheno_PC23.plate,
labels = c("mef02", "mef02_18", "mef02_fe", "jaffe"),
ncol = 4, nrow = 1, common.legend = TRUE, legend = "bottom")
1.4 Plot UMAP using PCs
- Only take forward eigenvec_sd02_fetal - seems to reduce spread well
- Input: 10 PCs (=63% of variance)
- uwot package (selected purely on the superior package naming https://github.com/jlmelville/uwot)
1.4.1 Age > 18
1.4.1.1 UMAP plots
eigenvec_sd02_age18.umap <- data.frame(uwot::umap(eigenvec_sd02_age18[,3:ncol(eigenvec_sd02_age18)]))
colnames(eigenvec_sd02_age18.umap) <- c("UMAP1", "UMAP2")
eigenvec_sd02_age18.umap$IID <- eigenvec_sd02_age18$IID
eigenvec_sd02_age18_pheno_umap <- join(eigenvec_sd02_age18_pheno, eigenvec_sd02_age18.umap, by = "IID", type = "inner")
eigenvec_sd02_age18_pheno_umap.group <- ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_umap.sex <- ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_umap.age <- ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_umap.race <- ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_age18_pheno_umap.plate <- ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
ggarrange(eigenvec_sd02_age18_pheno_umap.group, eigenvec_sd02_age18_pheno_umap.sex, eigenvec_sd02_age18_pheno_umap.age, eigenvec_sd02_age18_pheno_umap.race, eigenvec_sd02_age18_pheno_umap.plate, nrow = 3, ncol = 2)
1.4.1.2 Check batches
- look closer at the plates –> now look at slides
- do separately as so many factors for slides –> doesn’t visualise so well
1.4.1.3 Number per batch
- seems strange that there are so few on some batches?
table(eigenvec_sd02_age18_pheno_umap$slide)##
## 5771710013 5771710015 5771710017 5771710022 5771710023 5771710036 5771710037
## 9 7 12 11 9 8 7
## 5771710038 5806484004 5806484008 5806484010 5806484023 5806484024 5806484056
## 8 8 8 4 3 10 8
## 5806484057 5806636054 5815129004 5815129009 5815129011 5815129015 5815129018
## 8 1 11 12 11 10 12
## 5815129027 5815129028 5815188011 5815188020 5815188021 5815188022 5815188023
## 10 12 8 5 8 7 5
## 6229009001 6229009004 6229009040 6229009047 6229009049 6229009050 6229009056
## 5 8 9 4 7 10 5
## 6229009069 6229009079 6229009080 6229009083 6229009084 6229009100 6229009101
## 8 7 6 5 3 5 2
## 6229009106 6229009107 6229009112 6229009147 6229009151 6229009153 6229009162
## 1 2 6 1 4 1 9
## 6229009166 7810920047 7810920048 7810920075 7810920088 7810920089 7810920091
## 2 2 4 5 3 4 5
## 7810920102 7810920123 7810920128 7810920166 7810920178 7927554017 7927554041
## 1 5 1 1 1 2 1
## 7927554078 7927554080 7927554092 7927554093 7927554109 7927554120 7927554124
## 2 2 1 5 5 3 81.4.1.4 Visualise batches (UMAP)
- looks quite batchy …
- Slides inferred from .idat file names
ggplot(eigenvec_sd02_age18_pheno_umap, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = as.factor(slide))) + scale_color_viridis(discrete = TRUE, option = "D") + theme(legend.position = "bottom")
1.4.1.5 Overlay cell type proportions inferred by Jaffe et al.
eigenvec_sd02_age18_pheno_umap_ctp_jaffe <- join(eigenvec_sd02_age18_pheno_umap, ctp_jaffe, by = "IID", type = "inner")
eigenvec_sd02_age18_pheno_umap_ctp_jaffe.tmp <- eigenvec_sd02_age18_pheno_umap_ctp_jaffe[,c("IID", "UMAP1", "UMAP2", "comp_da_neuron", "comp_es", "comp_neun_neg", "comp_neun_pos", "comp_npc")]
eigenvec_sd02_age18_pheno_umap_ctp_jaffe.long <- melt(eigenvec_sd02_age18_pheno_umap_ctp_jaffe.tmp, id.vars = c("IID", "UMAP1", "UMAP2"), measure.vars = c("comp_da_neuron", "comp_es", "comp_neun_neg", "comp_neun_pos", "comp_npc"), variable.name = "celltype", value.name = "CTP")
ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe.long, aes(x = UMAP1, y = UMAP2, alpha = 0.8)) + geom_point(aes(colour = CTP)) + facet_wrap(~ celltype) + scale_color_viridis(discrete = FALSE, option = "D")
comp_da_neuron.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_da_neuron)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_es.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_es)) + scale_color_viridis(discrete = FALSE, option = "D") + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.comp_neun_neg.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_neun_neg)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_neun_pos.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_neun_pos)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_npc.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_npc)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
ggarrange(comp_da_neuron.umap, comp_es.umap, comp_neun_neg.umap, comp_neun_pos.umap, comp_npc.umap, nrow = 3, ncol = 2)
1.4.2 Fetal
1.4.2.1 UMAP plots
eigenvec_sd02_fetal.umap <- data.frame(uwot::umap(eigenvec_sd02_fetal[,3:ncol(eigenvec_sd02_fetal)]))
colnames(eigenvec_sd02_fetal.umap) <- c("UMAP1", "UMAP2")
eigenvec_sd02_fetal.umap$IID <- eigenvec_sd02_fetal$IID
eigenvec_sd02_fetal_pheno_umap <- join(eigenvec_sd02_fetal_pheno, eigenvec_sd02_fetal.umap, by = "IID", type = "inner")
eigenvec_sd02_fetal_pheno_umap.group <- ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = group)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_umap.sex <- ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = sex)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_umap.age <- ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = age)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_umap.race <- ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = race)) + geom_point() + theme(legend.position = "bottom")
eigenvec_sd02_fetal_pheno_umap.plate <- ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2, colour = plate)) + geom_point() + theme(legend.position = "bottom")
ggarrange(eigenvec_sd02_fetal_pheno_umap.group, eigenvec_sd02_fetal_pheno_umap.sex, eigenvec_sd02_fetal_pheno_umap.age, eigenvec_sd02_fetal_pheno_umap.race, eigenvec_sd02_fetal_pheno_umap.plate, nrow = 3, ncol = 2)
1.4.2.2 Check batches
- look closer at the plates –> now look at slides
- do separately as so many factors for slides –> doesn’t visualise so well
1.4.2.3 Number per batch
- seems strange that there are so few on some batches?
table(eigenvec_sd02_fetal_pheno_umap$slide)##
## 5806484010 5815188023 5815188024 5815188028 6229009083 6229009100 6229009145
## 4 6 7 8 1 1 4
## 6229009146 6229009147 7927554092
## 2 1 11.4.2.4 Visualise batches (UMAP)
- looks quite batchy …
- Slides inferred from .idat file names
ggplot(eigenvec_sd02_fetal_pheno_umap, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = as.factor(slide))) + scale_color_viridis(discrete = TRUE, option = "D") + theme(legend.position = "bottom")
1.4.2.5 Overlay cell type proportions inferred by Jaffe et al.
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe <- join(eigenvec_sd02_fetal_pheno_umap, ctp_jaffe, by = "IID", type = "inner")
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe.tmp <- eigenvec_sd02_fetal_pheno_umap_ctp_jaffe[,c("IID", "UMAP1", "UMAP2", "comp_da_neuron", "comp_es", "comp_neun_neg", "comp_neun_pos", "comp_npc")]
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe.long <- melt(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe.tmp, id.vars = c("IID", "UMAP1", "UMAP2"), measure.vars = c("comp_da_neuron", "comp_es", "comp_neun_neg", "comp_neun_pos", "comp_npc"), variable.name = "celltype", value.name = "CTP")
ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe.long, aes(x = UMAP1, y = UMAP2, alpha = 0.8)) + geom_point(aes(colour = CTP)) + facet_wrap(~ celltype) + scale_color_viridis(discrete = FALSE, option = "D")
comp_da_neuron.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_da_neuron)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_es.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_es)) + scale_color_viridis(discrete = FALSE, option = "D") + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.comp_neun_neg.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_neun_neg)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_neun_pos.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_neun_pos)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
comp_npc.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = comp_npc)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
ggarrange(comp_da_neuron.umap, comp_es.umap, comp_neun_neg.umap, comp_neun_pos.umap, comp_npc.umap, nrow = 3, ncol = 2)
1.4.2.6 Check linear models of PCs ~ batches
PC1
summary(lm(PC1 ~ as.factor(slide), data = eigenvec_sd02_pheno))##
## Call:
## lm(formula = PC1 ~ as.factor(slide), data = eigenvec_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.155923 -0.008228 -0.000967 0.006466 0.144048
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.012594 0.007897 1.595 0.111491
## as.factor(slide)5771710015 -0.009333 0.012486 -0.747 0.455171
## as.factor(slide)5771710017 -0.012132 0.011168 -1.086 0.277945
## as.factor(slide)5771710022 -0.019661 0.011168 -1.761 0.079023 .
## as.factor(slide)5771710023 -0.016474 0.011713 -1.407 0.160280
## as.factor(slide)5771710036 -0.012378 0.011713 -1.057 0.291183
## as.factor(slide)5771710037 -0.013747 0.012063 -1.140 0.255060
## as.factor(slide)5771710038 -0.015793 0.012063 -1.309 0.191145
## as.factor(slide)5806484002 0.013455 0.015794 0.852 0.394714
## as.factor(slide)5806484004 -0.021896 0.011713 -1.869 0.062244 .
## as.factor(slide)5806484008 -0.028123 0.012063 -2.331 0.020186 *
## as.factor(slide)5806484010 0.029452 0.011419 2.579 0.010228 *
## as.factor(slide)5806484023 -0.004597 0.011713 -0.393 0.694878
## as.factor(slide)5806484024 -0.022544 0.011713 -1.925 0.054911 .
## as.factor(slide)5806484056 -0.022620 0.012486 -1.812 0.070727 .
## as.factor(slide)5806484057 -0.020004 0.012486 -1.602 0.109858
## as.factor(slide)5806636054 -0.009144 0.011419 -0.801 0.423681
## as.factor(slide)5806636077 -0.010753 0.013678 -0.786 0.432172
## as.factor(slide)5815129004 -0.036372 0.011168 -3.257 0.001215 **
## as.factor(slide)5815129009 -0.028508 0.011168 -2.553 0.011030 *
## as.factor(slide)5815129011 -0.024612 0.011419 -2.155 0.031678 *
## as.factor(slide)5815129015 -0.032597 0.011419 -2.855 0.004514 **
## as.factor(slide)5815129018 -0.030837 0.011168 -2.761 0.006002 **
## as.factor(slide)5815129027 -0.028340 0.011713 -2.420 0.015948 *
## as.factor(slide)5815129028 -0.036447 0.011419 -3.192 0.001516 **
## as.factor(slide)5815188010 -0.020363 0.028472 -0.715 0.474877
## as.factor(slide)5815188011 -0.014356 0.011713 -1.226 0.220974
## as.factor(slide)5815188020 -0.011640 0.011419 -1.019 0.308594
## as.factor(slide)5815188021 -0.017984 0.012063 -1.491 0.136722
## as.factor(slide)5815188022 -0.016315 0.013010 -1.254 0.210506
## as.factor(slide)5815188023 0.063108 0.011168 5.651 2.89e-08 ***
## as.factor(slide)5815188024 0.146092 0.013010 11.229 < 2e-16 ***
## as.factor(slide)5815188028 0.125786 0.012063 10.428 < 2e-16 ***
## as.factor(slide)6229009001 -0.068002 0.013678 -4.972 9.57e-07 ***
## as.factor(slide)6229009004 -0.044793 0.012486 -3.587 0.000372 ***
## as.factor(slide)6229009040 -0.023605 0.012063 -1.957 0.051002 .
## as.factor(slide)6229009047 -0.035119 0.015794 -2.224 0.026686 *
## as.factor(slide)6229009049 -0.028639 0.013010 -2.201 0.028241 *
## as.factor(slide)6229009050 -0.029586 0.011419 -2.591 0.009892 **
## as.factor(slide)6229009056 -0.025432 0.014561 -1.747 0.081417 .
## as.factor(slide)6229009069 -0.015808 0.012486 -1.266 0.206170
## as.factor(slide)6229009079 -0.025884 0.013010 -1.990 0.047268 *
## as.factor(slide)6229009080 -0.031012 0.013678 -2.267 0.023858 *
## as.factor(slide)6229009083 -0.029276 0.014561 -2.011 0.044989 *
## as.factor(slide)6229009084 -0.020654 0.017658 -1.170 0.242779
## as.factor(slide)6229009100 0.000887 0.013010 0.068 0.945678
## as.factor(slide)6229009101 -0.036907 0.020893 -1.766 0.078020 .
## as.factor(slide)6229009106 -0.061978 0.028472 -2.177 0.030034 *
## as.factor(slide)6229009107 -0.035741 0.017658 -2.024 0.043573 *
## as.factor(slide)6229009112 -0.041417 0.013678 -3.028 0.002608 **
## as.factor(slide)6229009145 0.136976 0.017658 7.757 6.26e-14 ***
## as.factor(slide)6229009146 0.078669 0.020893 3.765 0.000189 ***
## as.factor(slide)6229009147 0.034128 0.017658 1.933 0.053919 .
## as.factor(slide)6229009151 -0.050466 0.015794 -3.195 0.001498 **
## as.factor(slide)6229009153 -0.058808 0.028472 -2.065 0.039473 *
## as.factor(slide)6229009162 -0.046537 0.012063 -3.858 0.000132 ***
## as.factor(slide)6229009166 -0.045790 0.020893 -2.192 0.028935 *
## as.factor(slide)7810920047 0.016833 0.017658 0.953 0.340986
## as.factor(slide)7810920048 -0.001833 0.013678 -0.134 0.893465
## as.factor(slide)7810920075 -0.031935 0.014561 -2.193 0.028823 *
## as.factor(slide)7810920088 -0.029491 0.017658 -1.670 0.095616 .
## as.factor(slide)7810920089 -0.025036 0.015794 -1.585 0.113651
## as.factor(slide)7810920091 -0.024775 0.013678 -1.811 0.070777 .
## as.factor(slide)7810920102 -0.025331 0.028472 -0.890 0.374138
## as.factor(slide)7810920123 -0.022649 0.014561 -1.555 0.120566
## as.factor(slide)7810920128 0.015522 0.013010 1.193 0.233487
## as.factor(slide)7810920166 -0.004008 0.017658 -0.227 0.820534
## as.factor(slide)7810920178 -0.026193 0.028472 -0.920 0.358109
## as.factor(slide)7927554017 -0.024730 0.020893 -1.184 0.237198
## as.factor(slide)7927554041 -0.018713 0.028472 -0.657 0.511370
## as.factor(slide)7927554078 -0.038674 0.020893 -1.851 0.064840 .
## as.factor(slide)7927554080 -0.035784 0.020893 -1.713 0.087480 .
## as.factor(slide)7927554092 0.051739 0.020893 2.476 0.013652 *
## as.factor(slide)7927554093 -0.037845 0.014561 -2.599 0.009666 **
## as.factor(slide)7927554109 -0.028654 0.014561 -1.968 0.049721 *
## as.factor(slide)7927554120 -0.034964 0.015794 -2.214 0.027363 *
## as.factor(slide)7927554124 -0.032877 0.012486 -2.633 0.008761 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02736 on 434 degrees of freedom
## Multiple R-squared: 0.6752, Adjusted R-squared: 0.6184
## F-statistic: 11.87 on 76 and 434 DF, p-value: < 2.2e-16summary(lm(PC1 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno))##
## Call:
## lm(formula = PC1 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.096757 -0.023634 -0.000765 0.019826 0.128832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.062797 0.013226 4.748 3.06e-06 ***
## as.factor(slide)5771710015 -0.024967 0.019996 -1.249 0.212691
## as.factor(slide)5771710017 -0.006865 0.017497 -0.392 0.695044
## as.factor(slide)5771710022 -0.050005 0.017835 -2.804 0.005346 **
## as.factor(slide)5771710023 -0.040079 0.018705 -2.143 0.032864 *
## as.factor(slide)5771710036 -0.034243 0.019281 -1.776 0.076645 .
## as.factor(slide)5771710037 -0.049806 0.019996 -2.491 0.013234 *
## as.factor(slide)5771710038 -0.036598 0.019281 -1.898 0.058538 .
## as.factor(slide)5806484004 -0.084768 0.019281 -4.397 1.48e-05 ***
## as.factor(slide)5806484008 -0.086536 0.019281 -4.488 9.91e-06 ***
## as.factor(slide)5806484010 -0.045794 0.023844 -1.921 0.055641 .
## as.factor(slide)5806484023 -0.016196 0.026453 -0.612 0.540777
## as.factor(slide)5806484024 -0.043980 0.018231 -2.412 0.016391 *
## as.factor(slide)5806484056 -0.047759 0.019281 -2.477 0.013744 *
## as.factor(slide)5806484057 -0.028300 0.019281 -1.468 0.143111
## as.factor(slide)5806636054 -0.033821 0.041826 -0.809 0.419310
## as.factor(slide)5815129004 -0.094502 0.017835 -5.299 2.12e-07 ***
## as.factor(slide)5815129009 -0.057800 0.017497 -3.303 0.001059 **
## as.factor(slide)5815129011 -0.039905 0.017835 -2.238 0.025913 *
## as.factor(slide)5815129015 -0.073144 0.018231 -4.012 7.44e-05 ***
## as.factor(slide)5815129018 -0.067776 0.017497 -3.874 0.000129 ***
## as.factor(slide)5815129027 -0.058738 0.018231 -3.222 0.001400 **
## as.factor(slide)5815129028 -0.084362 0.017497 -4.822 2.17e-06 ***
## as.factor(slide)5815188011 -0.060006 0.019281 -3.112 0.002018 **
## as.factor(slide)5815188020 -0.047360 0.022132 -2.140 0.033092 *
## as.factor(slide)5815188021 -0.035979 0.019281 -1.866 0.062915 .
## as.factor(slide)5815188022 -0.031195 0.019996 -1.560 0.119700
## as.factor(slide)5815188023 -0.016720 0.022132 -0.755 0.450495
## as.factor(slide)6229009001 -0.177453 0.022132 -8.018 1.84e-14 ***
## as.factor(slide)6229009004 -0.125744 0.019281 -6.522 2.58e-10 ***
## as.factor(slide)6229009040 -0.047717 0.018705 -2.551 0.011188 *
## as.factor(slide)6229009047 -0.081397 0.023844 -3.414 0.000720 ***
## as.factor(slide)6229009049 -0.063575 0.019996 -3.179 0.001615 **
## as.factor(slide)6229009050 -0.063924 0.018231 -3.506 0.000517 ***
## as.factor(slide)6229009056 -0.064106 0.022132 -2.897 0.004023 **
## as.factor(slide)6229009069 -0.007197 0.019281 -0.373 0.709199
## as.factor(slide)6229009079 -0.058917 0.019996 -2.946 0.003442 **
## as.factor(slide)6229009080 -0.077864 0.020913 -3.723 0.000231 ***
## as.factor(slide)6229009083 -0.063969 0.022132 -2.890 0.004101 **
## as.factor(slide)6229009084 -0.027580 0.026453 -1.043 0.297891
## as.factor(slide)6229009100 -0.085593 0.022132 -3.867 0.000132 ***
## as.factor(slide)6229009101 -0.082410 0.031019 -2.657 0.008269 **
## as.factor(slide)6229009106 -0.145262 0.041826 -3.473 0.000583 ***
## as.factor(slide)6229009107 -0.123861 0.031019 -3.993 8.03e-05 ***
## as.factor(slide)6229009112 -0.133149 0.020913 -6.367 6.40e-10 ***
## as.factor(slide)6229009147 -0.066930 0.041826 -1.600 0.110499
## as.factor(slide)6229009151 -0.129381 0.023844 -5.426 1.11e-07 ***
## as.factor(slide)6229009153 -0.162275 0.041826 -3.880 0.000126 ***
## as.factor(slide)6229009162 -0.119896 0.018705 -6.410 4.98e-10 ***
## as.factor(slide)6229009166 -0.115572 0.031019 -3.726 0.000229 ***
## as.factor(slide)7810920047 -0.068048 0.031019 -2.194 0.028942 *
## as.factor(slide)7810920048 -0.045675 0.023844 -1.916 0.056281 .
## as.factor(slide)7810920075 -0.091971 0.022132 -4.156 4.13e-05 ***
## as.factor(slide)7810920088 -0.079150 0.026453 -2.992 0.002977 **
## as.factor(slide)7810920089 -0.062282 0.023844 -2.612 0.009408 **
## as.factor(slide)7810920091 -0.062013 0.022132 -2.802 0.005377 **
## as.factor(slide)7810920102 -0.046707 0.041826 -1.117 0.264923
## as.factor(slide)7810920123 -0.065203 0.022132 -2.946 0.003445 **
## as.factor(slide)7810920128 -0.032997 0.041826 -0.789 0.430727
## as.factor(slide)7810920166 -0.012220 0.041826 -0.292 0.770340
## as.factor(slide)7810920178 -0.077918 0.041826 -1.863 0.063354 .
## as.factor(slide)7927554017 -0.048646 0.031019 -1.568 0.117764
## as.factor(slide)7927554041 -0.026134 0.041826 -0.625 0.532506
## as.factor(slide)7927554078 -0.150605 0.031019 -4.855 1.85e-06 ***
## as.factor(slide)7927554080 -0.103543 0.031019 -3.338 0.000939 ***
## as.factor(slide)7927554092 -0.137463 0.041826 -3.287 0.001122 **
## as.factor(slide)7927554093 -0.104216 0.022132 -4.709 3.66e-06 ***
## as.factor(slide)7927554109 -0.077165 0.022132 -3.487 0.000555 ***
## as.factor(slide)7927554120 -0.075119 0.026453 -2.840 0.004793 **
## as.factor(slide)7927554124 -0.088653 0.019281 -4.598 6.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03968 on 333 degrees of freedom
## Multiple R-squared: 0.4757, Adjusted R-squared: 0.3671
## F-statistic: 4.379 on 69 and 333 DF, p-value: < 2.2e-16summary(lm(PC1 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno))##
## Call:
## lm(formula = PC1 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.46625 -0.01142 0.00092 0.03223 0.17634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12659 0.05598 2.262 0.03268 *
## as.factor(slide)5815188023 -0.33501 0.07227 -4.636 9.58e-05 ***
## as.factor(slide)5815188024 -0.20407 0.07017 -2.908 0.00752 **
## as.factor(slide)5815188028 -0.15691 0.06856 -2.289 0.03082 *
## as.factor(slide)6229009083 -0.13013 0.12517 -1.040 0.30846
## as.factor(slide)6229009100 0.07049 0.12517 0.563 0.57833
## as.factor(slide)6229009145 0.07191 0.07916 0.908 0.37236
## as.factor(slide)6229009146 0.03978 0.09695 0.410 0.68510
## as.factor(slide)6229009147 0.11730 0.12517 0.937 0.35765
## as.factor(slide)7927554092 -0.16179 0.12517 -1.293 0.20798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.112 on 25 degrees of freedom
## Multiple R-squared: 0.6867, Adjusted R-squared: 0.5739
## F-statistic: 6.087 on 9 and 25 DF, p-value: 0.0001596summary(lm(PC1 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno))##
## Call:
## lm(formula = PC1 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.149163 -0.007463 -0.001256 0.004578 0.139028
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.309e-03 7.885e-03 0.546 0.58504
## as.factor(slide)5771710015 -1.040e-02 1.247e-02 -0.835 0.40444
## as.factor(slide)5771710017 -1.334e-02 1.115e-02 -1.196 0.23233
## as.factor(slide)5771710022 -1.833e-02 1.115e-02 -1.644 0.10092
## as.factor(slide)5771710023 -1.604e-02 1.170e-02 -1.371 0.17095
## as.factor(slide)5771710036 -1.260e-02 1.170e-02 -1.077 0.28199
## as.factor(slide)5771710037 -1.251e-02 1.205e-02 -1.038 0.29972
## as.factor(slide)5771710038 -1.586e-02 1.205e-02 -1.317 0.18858
## as.factor(slide)5806484002 1.060e-02 1.577e-02 0.672 0.50183
## as.factor(slide)5806484004 -1.591e-02 1.170e-02 -1.361 0.17430
## as.factor(slide)5806484008 -1.015e-02 1.205e-02 -0.843 0.39990
## as.factor(slide)5806484010 4.418e-02 1.115e-02 3.962 8.66e-05 ***
## as.factor(slide)5806484023 -5.115e-03 1.170e-02 -0.437 0.66206
## as.factor(slide)5806484024 -1.639e-02 1.170e-02 -1.401 0.16179
## as.factor(slide)5806484056 -1.918e-02 1.247e-02 -1.538 0.12474
## as.factor(slide)5806484057 -1.268e-02 1.247e-02 -1.017 0.30975
## as.factor(slide)5806636054 -6.796e-03 1.140e-02 -0.596 0.55147
## as.factor(slide)5806636077 -1.032e-02 1.366e-02 -0.755 0.45041
## as.factor(slide)5815129004 -2.024e-02 1.115e-02 -1.815 0.07025 .
## as.factor(slide)5815129009 -2.004e-02 1.115e-02 -1.797 0.07304 .
## as.factor(slide)5815129011 -1.904e-02 1.140e-02 -1.670 0.09560 .
## as.factor(slide)5815129015 -1.919e-02 1.115e-02 -1.721 0.08597 .
## as.factor(slide)5815129018 -2.255e-02 1.115e-02 -2.022 0.04377 *
## as.factor(slide)5815129027 -2.020e-02 1.140e-02 -1.772 0.07713 .
## as.factor(slide)5815129028 -2.051e-02 1.115e-02 -1.839 0.06655 .
## as.factor(slide)5815188010 -6.376e-03 1.454e-02 -0.439 0.66121
## as.factor(slide)5815188011 -8.841e-03 1.170e-02 -0.756 0.45012
## as.factor(slide)5815188020 -6.266e-03 1.140e-02 -0.550 0.58293
## as.factor(slide)5815188021 -1.390e-02 1.205e-02 -1.154 0.24901
## as.factor(slide)5815188022 -6.887e-03 1.299e-02 -0.530 0.59628
## as.factor(slide)5815188023 6.888e-02 1.115e-02 6.177 1.46e-09 ***
## as.factor(slide)5815188024 1.524e-01 1.299e-02 11.729 < 2e-16 ***
## as.factor(slide)5815188028 1.330e-01 1.205e-02 11.045 < 2e-16 ***
## as.factor(slide)6229009001 -1.826e-02 1.299e-02 -1.405 0.16065
## as.factor(slide)6229009004 -2.558e-02 1.247e-02 -2.051 0.04081 *
## as.factor(slide)6229009040 -2.148e-02 1.170e-02 -1.836 0.06699 .
## as.factor(slide)6229009047 -2.554e-02 1.577e-02 -1.620 0.10600
## as.factor(slide)6229009049 -2.040e-02 1.299e-02 -1.570 0.11712
## as.factor(slide)6229009050 -2.272e-02 1.140e-02 -1.992 0.04693 *
## as.factor(slide)6229009056 -1.979e-02 1.454e-02 -1.361 0.17421
## as.factor(slide)6229009069 -9.644e-03 1.247e-02 -0.774 0.43963
## as.factor(slide)6229009079 -1.870e-02 1.299e-02 -1.439 0.15082
## as.factor(slide)6229009080 -2.311e-02 1.366e-02 -1.692 0.09126 .
## as.factor(slide)6229009083 3.335e-03 1.366e-02 0.244 0.80722
## as.factor(slide)6229009084 -1.999e-02 1.763e-02 -1.134 0.25744
## as.factor(slide)6229009100 4.042e-03 1.247e-02 0.324 0.74596
## as.factor(slide)6229009101 -2.815e-02 2.086e-02 -1.350 0.17785
## as.factor(slide)6229009106 -3.484e-02 2.843e-02 -1.225 0.22108
## as.factor(slide)6229009107 -1.889e-02 1.763e-02 -1.071 0.28460
## as.factor(slide)6229009112 -2.767e-02 1.366e-02 -2.026 0.04340 *
## as.factor(slide)6229009145 1.413e-01 1.577e-02 8.957 < 2e-16 ***
## as.factor(slide)6229009146 1.032e-01 1.763e-02 5.855 9.24e-09 ***
## as.factor(slide)6229009147 4.306e-02 1.763e-02 2.442 0.01497 *
## as.factor(slide)6229009151 -3.093e-02 1.577e-02 -1.961 0.05050 .
## as.factor(slide)6229009153 -3.727e-02 2.843e-02 -1.311 0.19051
## as.factor(slide)6229009162 -2.889e-02 1.205e-02 -2.399 0.01687 *
## as.factor(slide)6229009166 -2.306e-02 2.086e-02 -1.105 0.26958
## as.factor(slide)7810920047 2.962e-02 1.763e-02 1.680 0.09370 .
## as.factor(slide)7810920048 6.199e-03 1.366e-02 0.454 0.65014
## as.factor(slide)7810920075 -1.857e-02 1.454e-02 -1.277 0.20231
## as.factor(slide)7810920088 -2.285e-02 1.577e-02 -1.449 0.14808
## as.factor(slide)7810920089 -2.028e-02 1.577e-02 -1.286 0.19921
## as.factor(slide)7810920091 -2.163e-02 1.366e-02 -1.584 0.11396
## as.factor(slide)7810920102 -2.121e-02 2.843e-02 -0.746 0.45612
## as.factor(slide)7810920123 -1.540e-02 1.454e-02 -1.059 0.29012
## as.factor(slide)7810920128 1.799e-02 1.299e-02 1.385 0.16683
## as.factor(slide)7810920166 -4.407e-05 1.763e-02 -0.002 0.99801
## as.factor(slide)7810920178 -2.005e-02 2.843e-02 -0.705 0.48111
## as.factor(slide)7927554017 -2.005e-02 2.086e-02 -0.961 0.33695
## as.factor(slide)7927554041 -2.010e-02 2.843e-02 -0.707 0.47998
## as.factor(slide)7927554078 -2.126e-02 2.086e-02 -1.019 0.30869
## as.factor(slide)7927554080 -2.356e-02 2.086e-02 -1.129 0.25937
## as.factor(slide)7927554092 5.714e-02 2.086e-02 2.739 0.00641 **
## as.factor(slide)7927554093 -2.243e-02 1.454e-02 -1.543 0.12354
## as.factor(slide)7927554109 -1.836e-02 1.454e-02 -1.263 0.20724
## as.factor(slide)7927554120 -2.006e-02 1.577e-02 -1.272 0.20414
## as.factor(slide)7927554124 -2.369e-02 1.247e-02 -1.900 0.05808 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02732 on 449 degrees of freedom
## Multiple R-squared: 0.665, Adjusted R-squared: 0.6083
## F-statistic: 11.73 on 76 and 449 DF, p-value: < 2.2e-16PC2
summary(lm(PC2 ~ as.factor(slide), data = eigenvec_sd02_pheno))##
## Call:
## lm(formula = PC2 ~ as.factor(slide), data = eigenvec_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.12277 -0.01761 0.00000 0.01631 0.11715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.045394 0.008560 5.303 1.82e-07 ***
## as.factor(slide)5771710015 -0.001431 0.013534 -0.106 0.915838
## as.factor(slide)5771710017 0.010041 0.012105 0.829 0.407313
## as.factor(slide)5771710022 -0.023604 0.012105 -1.950 0.051830 .
## as.factor(slide)5771710023 -0.014305 0.012696 -1.127 0.260487
## as.factor(slide)5771710036 -0.018410 0.012696 -1.450 0.147773
## as.factor(slide)5771710037 -0.027538 0.013075 -2.106 0.035768 *
## as.factor(slide)5771710038 -0.022456 0.013075 -1.717 0.086608 .
## as.factor(slide)5806484002 -0.017319 0.017120 -1.012 0.312270
## as.factor(slide)5806484004 -0.056258 0.012696 -4.431 1.19e-05 ***
## as.factor(slide)5806484008 -0.091247 0.013075 -6.979 1.12e-11 ***
## as.factor(slide)5806484010 -0.061455 0.012377 -4.965 9.89e-07 ***
## as.factor(slide)5806484023 -0.022598 0.012696 -1.780 0.075787 .
## as.factor(slide)5806484024 -0.023699 0.012696 -1.867 0.062627 .
## as.factor(slide)5806484056 -0.015798 0.013534 -1.167 0.243748
## as.factor(slide)5806484057 -0.016145 0.013534 -1.193 0.233558
## as.factor(slide)5806636054 -0.031438 0.012377 -2.540 0.011434 *
## as.factor(slide)5806636077 -0.033518 0.014826 -2.261 0.024269 *
## as.factor(slide)5815129004 -0.077801 0.012105 -6.427 3.43e-10 ***
## as.factor(slide)5815129009 -0.036191 0.012105 -2.990 0.002952 **
## as.factor(slide)5815129011 -0.021314 0.012377 -1.722 0.085776 .
## as.factor(slide)5815129015 -0.052966 0.012377 -4.279 2.31e-05 ***
## as.factor(slide)5815129018 -0.034681 0.012105 -2.865 0.004374 **
## as.factor(slide)5815129027 -0.032608 0.012696 -2.568 0.010553 *
## as.factor(slide)5815129028 -0.060478 0.012377 -4.886 1.45e-06 ***
## as.factor(slide)5815188010 -0.018540 0.030863 -0.601 0.548347
## as.factor(slide)5815188011 -0.039543 0.012696 -3.115 0.001964 **
## as.factor(slide)5815188020 -0.034262 0.012377 -2.768 0.005880 **
## as.factor(slide)5815188021 -0.023960 0.013075 -1.832 0.067568 .
## as.factor(slide)5815188022 -0.021160 0.014102 -1.500 0.134220
## as.factor(slide)5815188023 -0.043700 0.012105 -3.610 0.000342 ***
## as.factor(slide)5815188024 -0.078315 0.014102 -5.553 4.89e-08 ***
## as.factor(slide)5815188028 -0.085330 0.013075 -6.526 1.89e-10 ***
## as.factor(slide)6229009001 -0.218312 0.014826 -14.725 < 2e-16 ***
## as.factor(slide)6229009004 -0.117023 0.013534 -8.646 < 2e-16 ***
## as.factor(slide)6229009040 -0.028486 0.013075 -2.179 0.029899 *
## as.factor(slide)6229009047 -0.055598 0.017120 -3.248 0.001254 **
## as.factor(slide)6229009049 -0.038288 0.014102 -2.715 0.006892 **
## as.factor(slide)6229009050 -0.037358 0.012377 -3.018 0.002692 **
## as.factor(slide)6229009056 -0.040543 0.015783 -2.569 0.010542 *
## as.factor(slide)6229009069 -0.008968 0.013534 -0.663 0.507951
## as.factor(slide)6229009079 -0.035444 0.014102 -2.513 0.012320 *
## as.factor(slide)6229009080 -0.045240 0.014826 -3.051 0.002418 **
## as.factor(slide)6229009083 -0.041079 0.015783 -2.603 0.009567 **
## as.factor(slide)6229009084 -0.007602 0.019140 -0.397 0.691420
## as.factor(slide)6229009100 -0.065820 0.014102 -4.667 4.07e-06 ***
## as.factor(slide)6229009101 -0.055733 0.022647 -2.461 0.014245 *
## as.factor(slide)6229009106 -0.136138 0.030863 -4.411 1.30e-05 ***
## as.factor(slide)6229009107 -0.112246 0.019140 -5.864 8.94e-09 ***
## as.factor(slide)6229009112 -0.101766 0.014826 -6.864 2.32e-11 ***
## as.factor(slide)6229009145 -0.103019 0.019140 -5.382 1.21e-07 ***
## as.factor(slide)6229009146 -0.100982 0.022647 -4.459 1.05e-05 ***
## as.factor(slide)6229009147 -0.081549 0.019140 -4.261 2.50e-05 ***
## as.factor(slide)6229009151 -0.108933 0.017120 -6.363 5.03e-10 ***
## as.factor(slide)6229009153 -0.118494 0.030863 -3.839 0.000142 ***
## as.factor(slide)6229009162 -0.102685 0.013075 -7.853 3.21e-14 ***
## as.factor(slide)6229009166 -0.122516 0.022647 -5.410 1.04e-07 ***
## as.factor(slide)7810920047 -0.067526 0.019140 -3.528 0.000463 ***
## as.factor(slide)7810920048 -0.055229 0.014826 -3.725 0.000221 ***
## as.factor(slide)7810920075 -0.065382 0.015783 -4.142 4.13e-05 ***
## as.factor(slide)7810920088 -0.044253 0.019140 -2.312 0.021243 *
## as.factor(slide)7810920089 -0.029259 0.017120 -1.709 0.088149 .
## as.factor(slide)7810920091 -0.021457 0.014826 -1.447 0.148544
## as.factor(slide)7810920102 -0.001764 0.030863 -0.057 0.954455
## as.factor(slide)7810920123 -0.026481 0.015783 -1.678 0.094110 .
## as.factor(slide)7810920128 -0.047474 0.014102 -3.366 0.000829 ***
## as.factor(slide)7810920166 -0.039417 0.019140 -2.059 0.040053 *
## as.factor(slide)7810920178 -0.047377 0.030863 -1.535 0.125489
## as.factor(slide)7927554017 -0.020681 0.022647 -0.913 0.361646
## as.factor(slide)7927554041 0.002507 0.030863 0.081 0.935285
## as.factor(slide)7927554078 -0.110175 0.022647 -4.865 1.61e-06 ***
## as.factor(slide)7927554080 -0.068748 0.022647 -3.036 0.002545 **
## as.factor(slide)7927554092 -0.064452 0.022647 -2.846 0.004638 **
## as.factor(slide)7927554093 -0.075562 0.015783 -4.787 2.32e-06 ***
## as.factor(slide)7927554109 -0.049962 0.015783 -3.165 0.001657 **
## as.factor(slide)7927554120 -0.060889 0.017120 -3.557 0.000417 ***
## as.factor(slide)7927554124 -0.054213 0.013534 -4.006 7.28e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02965 on 434 degrees of freedom
## Multiple R-squared: 0.6184, Adjusted R-squared: 0.5516
## F-statistic: 9.255 on 76 and 434 DF, p-value: < 2.2e-16summary(lm(PC2 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno))##
## Call:
## lm(formula = PC2 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.106354 -0.017778 0.000111 0.020817 0.114419
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0057427 0.0116932 0.491 0.623667
## as.factor(slide)5771710015 0.0353702 0.0176784 2.001 0.046230 *
## as.factor(slide)5771710017 0.0158064 0.0154686 1.022 0.307600
## as.factor(slide)5771710022 0.0240569 0.0157671 1.526 0.128017
## as.factor(slide)5771710023 0.0312055 0.0165366 1.887 0.060023 .
## as.factor(slide)5771710036 0.0189482 0.0170456 1.112 0.267103
## as.factor(slide)5771710037 0.0296239 0.0176784 1.676 0.094733 .
## as.factor(slide)5771710038 0.0223664 0.0170456 1.312 0.190373
## as.factor(slide)5806484004 0.0123570 0.0170456 0.725 0.469001
## as.factor(slide)5806484008 -0.1022682 0.0170456 -6.000 5.16e-09 ***
## as.factor(slide)5806484010 -0.0249307 0.0210802 -1.183 0.237787
## as.factor(slide)5806484023 -0.0275811 0.0233864 -1.179 0.239093
## as.factor(slide)5806484024 0.0003323 0.0161179 0.021 0.983565
## as.factor(slide)5806484056 0.0331788 0.0170456 1.946 0.052439 .
## as.factor(slide)5806484057 -0.0197901 0.0170456 -1.161 0.246470
## as.factor(slide)5806636054 0.0236905 0.0369771 0.641 0.522171
## as.factor(slide)5815129004 -0.0396898 0.0157671 -2.517 0.012296 *
## as.factor(slide)5815129009 -0.0090931 0.0154686 -0.588 0.557037
## as.factor(slide)5815129011 -0.0001110 0.0157671 -0.007 0.994385
## as.factor(slide)5815129015 0.0068718 0.0161179 0.426 0.670133
## as.factor(slide)5815129018 0.0194132 0.0154686 1.255 0.210357
## as.factor(slide)5815129027 0.0038878 0.0161179 0.241 0.809539
## as.factor(slide)5815129028 -0.0200103 0.0154686 -1.294 0.196698
## as.factor(slide)5815188011 0.0146889 0.0170456 0.862 0.389448
## as.factor(slide)5815188020 0.0046034 0.0195664 0.235 0.814145
## as.factor(slide)5815188021 -0.0041185 0.0170456 -0.242 0.809227
## as.factor(slide)5815188022 -0.0099740 0.0176784 -0.564 0.573003
## as.factor(slide)5815188023 0.0090957 0.0195664 0.465 0.642333
## as.factor(slide)6229009001 -0.1714090 0.0195664 -8.760 < 2e-16 ***
## as.factor(slide)6229009004 -0.0845446 0.0170456 -4.960 1.13e-06 ***
## as.factor(slide)6229009040 -0.0042174 0.0165366 -0.255 0.798854
## as.factor(slide)6229009047 -0.0076481 0.0210802 -0.363 0.716975
## as.factor(slide)6229009049 -0.0029501 0.0176784 -0.167 0.867569
## as.factor(slide)6229009050 0.0067622 0.0161179 0.420 0.675086
## as.factor(slide)6229009056 0.0047499 0.0195664 0.243 0.808343
## as.factor(slide)6229009069 -0.0443270 0.0170456 -2.600 0.009724 **
## as.factor(slide)6229009079 -0.0015867 0.0176784 -0.090 0.928537
## as.factor(slide)6229009080 0.0126600 0.0184885 0.685 0.493980
## as.factor(slide)6229009083 0.0003908 0.0195664 0.020 0.984075
## as.factor(slide)6229009084 -0.0032465 0.0233864 -0.139 0.889675
## as.factor(slide)6229009100 -0.0142572 0.0195664 -0.729 0.466725
## as.factor(slide)6229009101 -0.0403161 0.0274229 -1.470 0.142463
## as.factor(slide)6229009106 -0.1297667 0.0369771 -3.509 0.000511 ***
## as.factor(slide)6229009107 -0.0634423 0.0274229 -2.313 0.021305 *
## as.factor(slide)6229009112 -0.0351226 0.0184885 -1.900 0.058337 .
## as.factor(slide)6229009147 -0.0659723 0.0369771 -1.784 0.075311 .
## as.factor(slide)6229009151 -0.0815907 0.0210802 -3.870 0.000131 ***
## as.factor(slide)6229009153 -0.0441296 0.0369771 -1.193 0.233550
## as.factor(slide)6229009162 -0.0738934 0.0165366 -4.468 1.08e-05 ***
## as.factor(slide)6229009166 -0.1403997 0.0274229 -5.120 5.18e-07 ***
## as.factor(slide)7810920047 -0.0654330 0.0274229 -2.386 0.017588 *
## as.factor(slide)7810920048 0.0001848 0.0210802 0.009 0.993011
## as.factor(slide)7810920075 -0.0043378 0.0195664 -0.222 0.824688
## as.factor(slide)7810920088 0.0204174 0.0233864 0.873 0.383265
## as.factor(slide)7810920089 0.0276463 0.0210802 1.311 0.190598
## as.factor(slide)7810920091 0.0349518 0.0195664 1.786 0.074958 .
## as.factor(slide)7810920102 0.0519857 0.0369771 1.406 0.160689
## as.factor(slide)7810920123 0.0421237 0.0195664 2.153 0.032048 *
## as.factor(slide)7810920128 0.0542779 0.0369771 1.468 0.143081
## as.factor(slide)7810920166 0.0096550 0.0369771 0.261 0.794171
## as.factor(slide)7810920178 0.0233002 0.0369771 0.630 0.529045
## as.factor(slide)7927554017 0.0215758 0.0274229 0.787 0.431970
## as.factor(slide)7927554041 0.0354718 0.0369771 0.959 0.338108
## as.factor(slide)7927554078 0.0199105 0.0274229 0.726 0.468317
## as.factor(slide)7927554080 0.0212687 0.0274229 0.776 0.438547
## as.factor(slide)7927554092 0.0721127 0.0369771 1.950 0.051991 .
## as.factor(slide)7927554093 0.0054606 0.0195664 0.279 0.780357
## as.factor(slide)7927554109 0.0147712 0.0195664 0.755 0.450826
## as.factor(slide)7927554120 0.0181893 0.0233864 0.778 0.437255
## as.factor(slide)7927554124 0.0241113 0.0170456 1.415 0.158145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03508 on 333 degrees of freedom
## Multiple R-squared: 0.5902, Adjusted R-squared: 0.5053
## F-statistic: 6.951 on 69 and 333 DF, p-value: < 2.2e-16summary(lm(PC2 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno))##
## Call:
## lm(formula = PC2 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57165 -0.00878 0.01482 0.04218 0.20740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.03228 0.07317 -0.441 0.6629
## as.factor(slide)5815188023 0.02357 0.09447 0.250 0.8050
## as.factor(slide)5815188024 0.17340 0.09173 1.890 0.0704 .
## as.factor(slide)5815188028 0.11008 0.08962 1.228 0.2308
## as.factor(slide)6229009083 0.04253 0.16362 0.260 0.7970
## as.factor(slide)6229009100 -0.07008 0.16362 -0.428 0.6721
## as.factor(slide)6229009145 -0.18682 0.10349 -1.805 0.0831 .
## as.factor(slide)6229009146 -0.13014 0.12674 -1.027 0.3144
## as.factor(slide)6229009147 -0.09697 0.16362 -0.593 0.5587
## as.factor(slide)7927554092 0.02602 0.16362 0.159 0.8749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1463 on 25 degrees of freedom
## Multiple R-squared: 0.4645, Adjusted R-squared: 0.2718
## F-statistic: 2.41 on 9 and 25 DF, p-value: 0.03987summary(lm(PC2 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno))##
## Call:
## lm(formula = PC2 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.092434 -0.018450 -0.001394 0.017459 0.094847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.037745 0.008818 4.280 2.28e-05 ***
## as.factor(slide)5771710015 -0.005209 0.013943 -0.374 0.708904
## as.factor(slide)5771710017 0.013345 0.012471 1.070 0.285166
## as.factor(slide)5771710022 -0.016545 0.012471 -1.327 0.185284
## as.factor(slide)5771710023 -0.012253 0.013080 -0.937 0.349356
## as.factor(slide)5771710036 -0.013858 0.013080 -1.059 0.289949
## as.factor(slide)5771710037 -0.019374 0.013470 -1.438 0.151042
## as.factor(slide)5771710038 -0.017525 0.013470 -1.301 0.193908
## as.factor(slide)5806484002 -0.023236 0.017637 -1.317 0.188346
## as.factor(slide)5806484004 -0.053720 0.013080 -4.107 4.76e-05 ***
## as.factor(slide)5806484008 -0.095367 0.013470 -7.080 5.59e-12 ***
## as.factor(slide)5806484010 -0.044799 0.012471 -3.592 0.000364 ***
## as.factor(slide)5806484023 -0.013220 0.013080 -1.011 0.312694
## as.factor(slide)5806484024 -0.017537 0.013080 -1.341 0.180673
## as.factor(slide)5806484056 -0.016324 0.013943 -1.171 0.242315
## as.factor(slide)5806484057 -0.006359 0.013943 -0.456 0.648559
## as.factor(slide)5806636054 -0.033001 0.012751 -2.588 0.009964 **
## as.factor(slide)5806636077 -0.021630 0.015274 -1.416 0.157425
## as.factor(slide)5815129004 -0.068774 0.012471 -5.515 5.91e-08 ***
## as.factor(slide)5815129009 -0.030991 0.012471 -2.485 0.013318 *
## as.factor(slide)5815129011 -0.010930 0.012751 -0.857 0.391824
## as.factor(slide)5815129015 -0.046447 0.012471 -3.724 0.000221 ***
## as.factor(slide)5815129018 -0.027913 0.012471 -2.238 0.025694 *
## as.factor(slide)5815129027 -0.025286 0.012751 -1.983 0.047973 *
## as.factor(slide)5815129028 -0.049839 0.012471 -3.996 7.52e-05 ***
## as.factor(slide)5815188010 -0.013546 0.016260 -0.833 0.405246
## as.factor(slide)5815188011 -0.028129 0.013080 -2.151 0.032043 *
## as.factor(slide)5815188020 -0.020577 0.012751 -1.614 0.107282
## as.factor(slide)5815188021 -0.012145 0.013470 -0.902 0.367749
## as.factor(slide)5815188022 0.003600 0.014528 0.248 0.804416
## as.factor(slide)5815188023 -0.019223 0.012471 -1.541 0.123920
## as.factor(slide)5815188024 -0.031193 0.014528 -2.147 0.032322 *
## as.factor(slide)5815188028 -0.044082 0.013470 -3.273 0.001148 **
## as.factor(slide)6229009001 -0.160322 0.014528 -11.035 < 2e-16 ***
## as.factor(slide)6229009004 -0.102878 0.013943 -7.378 7.81e-13 ***
## as.factor(slide)6229009040 -0.023312 0.013080 -1.782 0.075377 .
## as.factor(slide)6229009047 -0.045183 0.017637 -2.562 0.010735 *
## as.factor(slide)6229009049 -0.031654 0.014528 -2.179 0.029866 *
## as.factor(slide)6229009050 -0.037868 0.012751 -2.970 0.003140 **
## as.factor(slide)6229009056 -0.032153 0.016260 -1.977 0.048603 *
## as.factor(slide)6229009069 0.006128 0.013943 0.440 0.660509
## as.factor(slide)6229009079 -0.029382 0.014528 -2.022 0.043724 *
## as.factor(slide)6229009080 -0.040086 0.015274 -2.625 0.008973 **
## as.factor(slide)6229009083 -0.033171 0.015274 -2.172 0.030394 *
## as.factor(slide)6229009084 -0.015098 0.019718 -0.766 0.444263
## as.factor(slide)6229009100 -0.075171 0.013943 -5.391 1.13e-07 ***
## as.factor(slide)6229009101 -0.088973 0.023331 -3.814 0.000156 ***
## as.factor(slide)6229009106 -0.161435 0.031795 -5.077 5.62e-07 ***
## as.factor(slide)6229009107 -0.125716 0.019718 -6.376 4.53e-10 ***
## as.factor(slide)6229009112 -0.120263 0.015274 -7.874 2.61e-14 ***
## as.factor(slide)6229009145 -0.095863 0.017637 -5.435 8.99e-08 ***
## as.factor(slide)6229009146 -0.101096 0.019718 -5.127 4.38e-07 ***
## as.factor(slide)6229009147 -0.075567 0.019718 -3.832 0.000145 ***
## as.factor(slide)6229009151 -0.100613 0.017637 -5.705 2.12e-08 ***
## as.factor(slide)6229009153 -0.134742 0.031795 -4.238 2.74e-05 ***
## as.factor(slide)6229009162 -0.111447 0.013470 -8.274 1.49e-15 ***
## as.factor(slide)6229009166 -0.116997 0.023331 -5.015 7.66e-07 ***
## as.factor(slide)7810920047 -0.058911 0.019718 -2.988 0.002966 **
## as.factor(slide)7810920048 -0.034371 0.015274 -2.250 0.024909 *
## as.factor(slide)7810920075 -0.044331 0.016260 -2.726 0.006654 **
## as.factor(slide)7810920088 -0.040017 0.017637 -2.269 0.023742 *
## as.factor(slide)7810920089 -0.026655 0.017637 -1.511 0.131406
## as.factor(slide)7810920091 -0.026652 0.015274 -1.745 0.081672 .
## as.factor(slide)7810920102 -0.009273 0.031795 -0.292 0.770691
## as.factor(slide)7810920123 -0.030630 0.016260 -1.884 0.060249 .
## as.factor(slide)7810920128 -0.041705 0.014528 -2.871 0.004290 **
## as.factor(slide)7810920166 -0.036713 0.019718 -1.862 0.063274 .
## as.factor(slide)7810920178 -0.042792 0.031795 -1.346 0.179020
## as.factor(slide)7927554017 -0.017348 0.023331 -0.744 0.457527
## as.factor(slide)7927554041 0.003204 0.031795 0.101 0.919772
## as.factor(slide)7927554078 -0.091016 0.023331 -3.901 0.000110 ***
## as.factor(slide)7927554080 -0.055330 0.023331 -2.372 0.018135 *
## as.factor(slide)7927554092 -0.053741 0.023331 -2.303 0.021713 *
## as.factor(slide)7927554093 -0.054439 0.016260 -3.348 0.000882 ***
## as.factor(slide)7927554109 -0.038074 0.016260 -2.342 0.019639 *
## as.factor(slide)7927554120 -0.036922 0.017637 -2.094 0.036865 *
## as.factor(slide)7927554124 -0.046802 0.013943 -3.357 0.000856 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03055 on 449 degrees of freedom
## Multiple R-squared: 0.581, Adjusted R-squared: 0.5101
## F-statistic: 8.193 on 76 and 449 DF, p-value: < 2.2e-16PC3
summary(lm(PC3 ~ as.factor(slide), data = eigenvec_sd02_pheno))##
## Call:
## lm(formula = PC3 ~ as.factor(slide), data = eigenvec_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.083722 -0.023321 -0.001714 0.019326 0.121817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0011188 0.0108702 -0.103 0.918072
## as.factor(slide)5771710015 -0.0173928 0.0171873 -1.012 0.312123
## as.factor(slide)5771710017 0.0035243 0.0153728 0.229 0.818777
## as.factor(slide)5771710022 -0.0195257 0.0153728 -1.270 0.204711
## as.factor(slide)5771710023 -0.0233872 0.0161231 -1.451 0.147630
## as.factor(slide)5771710036 -0.0237666 0.0161231 -1.474 0.141187
## as.factor(slide)5771710037 -0.0268992 0.0166045 -1.620 0.105960
## as.factor(slide)5771710038 -0.0227048 0.0166045 -1.367 0.172211
## as.factor(slide)5806484002 -0.0505638 0.0217404 -2.326 0.020489 *
## as.factor(slide)5806484004 -0.0226809 0.0161231 -1.407 0.160222
## as.factor(slide)5806484008 0.0683983 0.0166045 4.119 4.55e-05 ***
## as.factor(slide)5806484010 0.0090152 0.0157183 0.574 0.566571
## as.factor(slide)5806484023 0.0069352 0.0161231 0.430 0.667307
## as.factor(slide)5806484024 0.0067073 0.0161231 0.416 0.677610
## as.factor(slide)5806484056 -0.0178235 0.0171873 -1.037 0.300305
## as.factor(slide)5806484057 0.0274632 0.0171873 1.598 0.110798
## as.factor(slide)5806636054 -0.0122690 0.0157183 -0.781 0.435489
## as.factor(slide)5806636077 -0.0282518 0.0188277 -1.501 0.134201
## as.factor(slide)5815129004 0.0294935 0.0153728 1.919 0.055696 .
## as.factor(slide)5815129009 0.0118959 0.0153728 0.774 0.439453
## as.factor(slide)5815129011 0.0107789 0.0157183 0.686 0.493232
## as.factor(slide)5815129015 -0.0055758 0.0157183 -0.355 0.722962
## as.factor(slide)5815129018 -0.0115064 0.0153728 -0.748 0.454570
## as.factor(slide)5815129027 0.0010366 0.0161231 0.064 0.948768
## as.factor(slide)5815129028 0.0155098 0.0157183 0.987 0.324321
## as.factor(slide)5815188010 0.0503012 0.0391930 1.283 0.200029
## as.factor(slide)5815188011 -0.0177450 0.0161231 -1.101 0.271684
## as.factor(slide)5815188020 -0.0075644 0.0157183 -0.481 0.630581
## as.factor(slide)5815188021 0.0034840 0.0166045 0.210 0.833905
## as.factor(slide)5815188022 0.0207116 0.0179087 1.157 0.248110
## as.factor(slide)5815188023 0.0018735 0.0153728 0.122 0.903058
## as.factor(slide)5815188024 0.0105281 0.0179087 0.588 0.556921
## as.factor(slide)5815188028 0.0112877 0.0166045 0.680 0.496994
## as.factor(slide)6229009001 0.1087603 0.0188277 5.777 1.46e-08 ***
## as.factor(slide)6229009004 0.0474644 0.0171873 2.762 0.005996 **
## as.factor(slide)6229009040 0.0066216 0.0166045 0.399 0.690251
## as.factor(slide)6229009047 0.0035095 0.0217404 0.161 0.871832
## as.factor(slide)6229009049 0.0039354 0.0179087 0.220 0.826170
## as.factor(slide)6229009050 -0.0093722 0.0157183 -0.596 0.551310
## as.factor(slide)6229009056 -0.0067579 0.0200436 -0.337 0.736161
## as.factor(slide)6229009069 0.0536302 0.0171873 3.120 0.001927 **
## as.factor(slide)6229009079 0.0036380 0.0179087 0.203 0.839120
## as.factor(slide)6229009080 -0.0130692 0.0188277 -0.694 0.487963
## as.factor(slide)6229009083 0.0008821 0.0200436 0.044 0.964917
## as.factor(slide)6229009084 0.0143932 0.0243065 0.592 0.554056
## as.factor(slide)6229009100 0.0033544 0.0179087 0.187 0.851510
## as.factor(slide)6229009101 0.0320777 0.0287598 1.115 0.265311
## as.factor(slide)6229009106 0.0905745 0.0391930 2.311 0.021302 *
## as.factor(slide)6229009107 0.0097972 0.0243065 0.403 0.687096
## as.factor(slide)6229009112 0.0037691 0.0188277 0.200 0.841425
## as.factor(slide)6229009145 0.0334721 0.0243065 1.377 0.169196
## as.factor(slide)6229009146 -0.0039261 0.0287598 -0.137 0.891480
## as.factor(slide)6229009147 0.0405623 0.0243065 1.669 0.095882 .
## as.factor(slide)6229009151 0.0512704 0.0217404 2.358 0.018801 *
## as.factor(slide)6229009153 0.0149629 0.0391930 0.382 0.702816
## as.factor(slide)6229009162 0.0468917 0.0166045 2.824 0.004961 **
## as.factor(slide)6229009166 0.0972518 0.0287598 3.382 0.000786 ***
## as.factor(slide)7810920047 0.0224149 0.0243065 0.922 0.356949
## as.factor(slide)7810920048 -0.0125681 0.0188277 -0.668 0.504787
## as.factor(slide)7810920075 -0.0063961 0.0200436 -0.319 0.749797
## as.factor(slide)7810920088 -0.0243124 0.0243065 -1.000 0.317749
## as.factor(slide)7810920089 -0.0239047 0.0217404 -1.100 0.272136
## as.factor(slide)7810920091 -0.0347105 0.0188277 -1.844 0.065925 .
## as.factor(slide)7810920102 -0.0271506 0.0391930 -0.693 0.488843
## as.factor(slide)7810920123 -0.0343849 0.0200436 -1.716 0.086967 .
## as.factor(slide)7810920128 -0.0543430 0.0179087 -3.034 0.002555 **
## as.factor(slide)7810920166 -0.0522474 0.0243065 -2.150 0.032145 *
## as.factor(slide)7810920178 -0.0283403 0.0391930 -0.723 0.470011
## as.factor(slide)7927554017 -0.0099782 0.0287598 -0.347 0.728799
## as.factor(slide)7927554041 -0.0152087 0.0391930 -0.388 0.698172
## as.factor(slide)7927554078 -0.0483650 0.0287598 -1.682 0.093349 .
## as.factor(slide)7927554080 -0.0294344 0.0287598 -1.023 0.306663
## as.factor(slide)7927554092 -0.0384697 0.0287598 -1.338 0.181722
## as.factor(slide)7927554093 -0.0136951 0.0200436 -0.683 0.494804
## as.factor(slide)7927554109 -0.0163036 0.0200436 -0.813 0.416431
## as.factor(slide)7927554120 -0.0057948 0.0217404 -0.267 0.789944
## as.factor(slide)7927554124 -0.0266722 0.0171873 -1.552 0.121426
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03766 on 434 degrees of freedom
## Multiple R-squared: 0.3846, Adjusted R-squared: 0.2769
## F-statistic: 3.569 on 76 and 434 DF, p-value: < 2.2e-16summary(lm(PC3 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno))##
## Call:
## lm(formula = PC3 ~ as.factor(slide), data = eigenvec_sd02_age18_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.118718 -0.009969 0.000000 0.008636 0.118771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0206769 0.0067848 -3.048 0.002492 **
## as.factor(slide)5771710015 0.0027479 0.0102576 0.268 0.788949
## as.factor(slide)5771710017 0.0026960 0.0089754 0.300 0.764077
## as.factor(slide)5771710022 0.0009628 0.0091486 0.105 0.916246
## as.factor(slide)5771710023 -0.0075201 0.0095951 -0.784 0.433750
## as.factor(slide)5771710036 -0.0001704 0.0098904 -0.017 0.986266
## as.factor(slide)5771710037 0.0044342 0.0102576 0.432 0.665811
## as.factor(slide)5771710038 -0.0085231 0.0098904 -0.862 0.389443
## as.factor(slide)5806484004 -0.0047447 0.0098904 -0.480 0.631735
## as.factor(slide)5806484008 -0.0721901 0.0098904 -7.299 2.15e-12 ***
## as.factor(slide)5806484010 -0.0116631 0.0122314 -0.954 0.341010
## as.factor(slide)5806484023 -0.0302780 0.0135695 -2.231 0.026325 *
## as.factor(slide)5806484024 0.0282773 0.0093522 3.024 0.002692 **
## as.factor(slide)5806484056 0.0219614 0.0098904 2.220 0.027059 *
## as.factor(slide)5806484057 0.0376597 0.0098904 3.808 0.000167 ***
## as.factor(slide)5806636054 -0.0340390 0.0214553 -1.587 0.113574
## as.factor(slide)5815129004 -0.0308536 0.0091486 -3.373 0.000833 ***
## as.factor(slide)5815129009 0.0049498 0.0089754 0.551 0.581674
## as.factor(slide)5815129011 0.0157928 0.0091486 1.726 0.085229 .
## as.factor(slide)5815129015 0.0038162 0.0093522 0.408 0.683494
## as.factor(slide)5815129018 0.0129406 0.0089754 1.442 0.150304
## as.factor(slide)5815129027 0.0207926 0.0093522 2.223 0.026867 *
## as.factor(slide)5815129028 0.0409617 0.0089754 4.564 7.08e-06 ***
## as.factor(slide)5815188011 -0.0019923 0.0098904 -0.201 0.840476
## as.factor(slide)5815188020 0.0025035 0.0113531 0.221 0.825607
## as.factor(slide)5815188021 -0.0062564 0.0098904 -0.633 0.527448
## as.factor(slide)5815188022 0.0207620 0.0102576 2.024 0.043762 *
## as.factor(slide)5815188023 0.0096351 0.0113531 0.849 0.396672
## as.factor(slide)6229009001 0.1066514 0.0113531 9.394 < 2e-16 ***
## as.factor(slide)6229009004 -0.0004897 0.0098904 -0.050 0.960540
## as.factor(slide)6229009040 0.0293303 0.0095951 3.057 0.002418 **
## as.factor(slide)6229009047 0.0320210 0.0122314 2.618 0.009250 **
## as.factor(slide)6229009049 0.0351427 0.0102576 3.426 0.000689 ***
## as.factor(slide)6229009050 0.0419299 0.0093522 4.483 1.01e-05 ***
## as.factor(slide)6229009056 0.0348482 0.0113531 3.069 0.002320 **
## as.factor(slide)6229009069 0.0545434 0.0098904 5.515 7.02e-08 ***
## as.factor(slide)6229009079 0.0366329 0.0102576 3.571 0.000408 ***
## as.factor(slide)6229009080 0.0355049 0.0107277 3.310 0.001036 **
## as.factor(slide)6229009083 0.0269746 0.0113531 2.376 0.018068 *
## as.factor(slide)6229009084 -0.0146660 0.0135695 -1.081 0.280567
## as.factor(slide)6229009100 -0.0462394 0.0113531 -4.073 5.81e-05 ***
## as.factor(slide)6229009101 -0.0818767 0.0159117 -5.146 4.56e-07 ***
## as.factor(slide)6229009106 0.0415944 0.0214553 1.939 0.053388 .
## as.factor(slide)6229009107 -0.0657174 0.0159117 -4.130 4.59e-05 ***
## as.factor(slide)6229009112 -0.0731705 0.0107277 -6.821 4.28e-11 ***
## as.factor(slide)6229009147 -0.0267478 0.0214553 -1.247 0.213393
## as.factor(slide)6229009151 -0.0442838 0.0122314 -3.620 0.000340 ***
## as.factor(slide)6229009153 -0.0289658 0.0214553 -1.350 0.177916
## as.factor(slide)6229009162 -0.0330482 0.0095951 -3.444 0.000646 ***
## as.factor(slide)6229009166 -0.0395912 0.0159117 -2.488 0.013328 *
## as.factor(slide)7810920047 0.1510512 0.0159117 9.493 < 2e-16 ***
## as.factor(slide)7810920048 0.0884394 0.0122314 7.231 3.32e-12 ***
## as.factor(slide)7810920075 0.1237393 0.0113531 10.899 < 2e-16 ***
## as.factor(slide)7810920088 0.0811932 0.0135695 5.983 5.64e-09 ***
## as.factor(slide)7810920089 0.0690426 0.0122314 5.645 3.55e-08 ***
## as.factor(slide)7810920091 0.0664004 0.0113531 5.849 1.18e-08 ***
## as.factor(slide)7810920102 0.0642807 0.0214553 2.996 0.002940 **
## as.factor(slide)7810920123 0.0823802 0.0113531 7.256 2.82e-12 ***
## as.factor(slide)7810920128 0.0567296 0.0214553 2.644 0.008579 **
## as.factor(slide)7810920166 0.0823127 0.0214553 3.836 0.000149 ***
## as.factor(slide)7810920178 0.0765063 0.0214553 3.566 0.000416 ***
## as.factor(slide)7927554017 0.0878568 0.0159117 5.522 6.78e-08 ***
## as.factor(slide)7927554041 0.0716784 0.0214553 3.341 0.000930 ***
## as.factor(slide)7927554078 0.1446794 0.0159117 9.093 < 2e-16 ***
## as.factor(slide)7927554080 0.1279431 0.0159117 8.041 1.58e-14 ***
## as.factor(slide)7927554092 0.1140251 0.0214553 5.315 1.96e-07 ***
## as.factor(slide)7927554093 0.1409568 0.0113531 12.416 < 2e-16 ***
## as.factor(slide)7927554109 0.1153304 0.0113531 10.159 < 2e-16 ***
## as.factor(slide)7927554120 0.1272110 0.0135695 9.375 < 2e-16 ***
## as.factor(slide)7927554124 0.1081595 0.0098904 10.936 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02035 on 333 degrees of freedom
## Multiple R-squared: 0.862, Adjusted R-squared: 0.8335
## F-statistic: 30.16 on 69 and 333 DF, p-value: < 2.2e-16summary(lm(PC3 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno))##
## Call:
## lm(formula = PC3 ~ as.factor(slide), data = eigenvec_sd02_fetal_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.34651 -0.09329 0.00000 0.05083 0.60359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06900 0.09212 -0.749 0.461
## as.factor(slide)5815188023 0.07481 0.11893 0.629 0.535
## as.factor(slide)5815188024 0.06817 0.11548 0.590 0.560
## as.factor(slide)5815188028 0.10548 0.11282 0.935 0.359
## as.factor(slide)6229009083 -0.11778 0.20599 -0.572 0.573
## as.factor(slide)6229009100 -0.08895 0.20599 -0.432 0.670
## as.factor(slide)6229009145 0.08426 0.13028 0.647 0.524
## as.factor(slide)6229009146 0.05815 0.15956 0.364 0.719
## as.factor(slide)6229009147 0.08390 0.20599 0.407 0.687
## as.factor(slide)7927554092 0.31446 0.20599 1.527 0.139
##
## Residual standard error: 0.1842 on 25 degrees of freedom
## Multiple R-squared: 0.1514, Adjusted R-squared: -0.1541
## F-statistic: 0.4955 on 9 and 25 DF, p-value: 0.8636summary(lm(PC3 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno))##
## Call:
## lm(formula = PC3 ~ as.factor(slide), data = eigenvec_jaffe_sd02_pheno)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076794 -0.020122 -0.001515 0.016002 0.110748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0015023 0.0094348 0.159 0.873558
## as.factor(slide)5771710015 -0.0183682 0.0149177 -1.231 0.218855
## as.factor(slide)5771710017 -0.0011119 0.0133428 -0.083 0.933625
## as.factor(slide)5771710022 -0.0215706 0.0133428 -1.617 0.106658
## as.factor(slide)5771710023 -0.0231526 0.0139941 -1.654 0.098733 .
## as.factor(slide)5771710036 -0.0246861 0.0139941 -1.764 0.078405 .
## as.factor(slide)5771710037 -0.0313433 0.0144119 -2.175 0.030164 *
## as.factor(slide)5771710038 -0.0212001 0.0144119 -1.471 0.141988
## as.factor(slide)5806484002 -0.0431559 0.0188696 -2.287 0.022657 *
## as.factor(slide)5806484004 -0.0181136 0.0139941 -1.294 0.196200
## as.factor(slide)5806484008 0.0915242 0.0144119 6.351 5.26e-10 ***
## as.factor(slide)5806484010 0.0099247 0.0133428 0.744 0.457373
## as.factor(slide)5806484023 0.0161199 0.0139941 1.152 0.249970
## as.factor(slide)5806484024 -0.0004183 0.0139941 -0.030 0.976169
## as.factor(slide)5806484056 -0.0240263 0.0149177 -1.611 0.107973
## as.factor(slide)5806484057 0.0175715 0.0149177 1.178 0.239464
## as.factor(slide)5806636054 -0.0007795 0.0136427 -0.057 0.954464
## as.factor(slide)5806636077 -0.0274382 0.0163415 -1.679 0.093839 .
## as.factor(slide)5815129004 0.0334782 0.0133428 2.509 0.012456 *
## as.factor(slide)5815129009 0.0094631 0.0133428 0.709 0.478552
## as.factor(slide)5815129011 0.0018414 0.0136427 0.135 0.892696
## as.factor(slide)5815129015 -0.0104732 0.0133428 -0.785 0.432905
## as.factor(slide)5815129018 -0.0198449 0.0133428 -1.487 0.137634
## as.factor(slide)5815129027 -0.0065734 0.0136427 -0.482 0.630166
## as.factor(slide)5815129028 0.0069788 0.0133428 0.523 0.601204
## as.factor(slide)5815188010 0.0133147 0.0173969 0.765 0.444467
## as.factor(slide)5815188011 -0.0234857 0.0139941 -1.678 0.093992 .
## as.factor(slide)5815188020 -0.0126118 0.0136427 -0.924 0.355757
## as.factor(slide)5815188021 -0.0017641 0.0144119 -0.122 0.902634
## as.factor(slide)5815188022 -0.0002928 0.0155439 -0.019 0.984981
## as.factor(slide)5815188023 -0.0166619 0.0133428 -1.249 0.212405
## as.factor(slide)5815188024 -0.0208674 0.0155439 -1.342 0.180119
## as.factor(slide)5815188028 -0.0108551 0.0144119 -0.753 0.451720
## as.factor(slide)6229009001 0.0799988 0.0155439 5.147 3.97e-07 ***
## as.factor(slide)6229009004 0.0551002 0.0149177 3.694 0.000248 ***
## as.factor(slide)6229009040 -0.0003564 0.0139941 -0.025 0.979690
## as.factor(slide)6229009047 0.0001961 0.0188696 0.010 0.991715
## as.factor(slide)6229009049 -0.0004593 0.0155439 -0.030 0.976440
## as.factor(slide)6229009050 -0.0119353 0.0136427 -0.875 0.382122
## as.factor(slide)6229009056 -0.0099792 0.0173969 -0.574 0.566514
## as.factor(slide)6229009069 0.0413650 0.0149177 2.773 0.005788 **
## as.factor(slide)6229009079 -0.0011548 0.0155439 -0.074 0.940810
## as.factor(slide)6229009080 -0.0170259 0.0163415 -1.042 0.298029
## as.factor(slide)6229009083 -0.0032263 0.0163415 -0.197 0.843579
## as.factor(slide)6229009084 0.0218462 0.0210968 1.036 0.300984
## as.factor(slide)6229009100 0.0274872 0.0149177 1.843 0.066049 .
## as.factor(slide)6229009101 0.0653511 0.0249621 2.618 0.009143 **
## as.factor(slide)6229009106 0.1098497 0.0340176 3.229 0.001332 **
## as.factor(slide)6229009107 0.0395715 0.0210968 1.876 0.061344 .
## as.factor(slide)6229009112 0.0351445 0.0163415 2.151 0.032039 *
## as.factor(slide)6229009145 0.0497682 0.0188696 2.637 0.008642 **
## as.factor(slide)6229009146 0.0288833 0.0210968 1.369 0.171658
## as.factor(slide)6229009147 0.0528690 0.0210968 2.506 0.012563 *
## as.factor(slide)6229009151 0.0630915 0.0188696 3.344 0.000896 ***
## as.factor(slide)6229009153 0.0346271 0.0340176 1.018 0.309266
## as.factor(slide)6229009162 0.0662782 0.0144119 4.599 5.53e-06 ***
## as.factor(slide)6229009166 0.1114727 0.0249621 4.466 1.01e-05 ***
## as.factor(slide)7810920047 0.0173749 0.0210968 0.824 0.410615
## as.factor(slide)7810920048 -0.0285358 0.0163415 -1.746 0.081458 .
## as.factor(slide)7810920075 -0.0256199 0.0173969 -1.473 0.141541
## as.factor(slide)7810920088 -0.0301141 0.0188696 -1.596 0.111213
## as.factor(slide)7810920089 -0.0298889 0.0188696 -1.584 0.113904
## as.factor(slide)7810920091 -0.0403263 0.0163415 -2.468 0.013970 *
## as.factor(slide)7810920102 -0.0406244 0.0340176 -1.194 0.233024
## as.factor(slide)7810920123 -0.0442703 0.0173969 -2.545 0.011270 *
## as.factor(slide)7810920128 -0.0608998 0.0155439 -3.918 0.000103 ***
## as.factor(slide)7810920166 -0.0623629 0.0210968 -2.956 0.003280 **
## as.factor(slide)7810920178 -0.0302890 0.0340176 -0.890 0.373732
## as.factor(slide)7927554017 -0.0208255 0.0249621 -0.834 0.404565
## as.factor(slide)7927554041 -0.0234184 0.0340176 -0.688 0.491544
## as.factor(slide)7927554078 -0.0614372 0.0249621 -2.461 0.014222 *
## as.factor(slide)7927554080 -0.0433592 0.0249621 -1.737 0.083073 .
## as.factor(slide)7927554092 -0.0550576 0.0249621 -2.206 0.027915 *
## as.factor(slide)7927554093 -0.0352741 0.0173969 -2.028 0.043190 *
## as.factor(slide)7927554109 -0.0319275 0.0173969 -1.835 0.067132 .
## as.factor(slide)7927554120 -0.0318497 0.0188696 -1.688 0.092127 .
## as.factor(slide)7927554124 -0.0367302 0.0149177 -2.462 0.014184 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03268 on 449 degrees of freedom
## Multiple R-squared: 0.5204, Adjusted R-squared: 0.4392
## F-statistic: 6.41 on 76 and 449 DF, p-value: < 2.2e-161.5 Unsupervised cell clusters (ReFACTor in GLINT): age18
Settings for ReFACTor
- include as covariates: age, sex, race, plate
- needed to use model.matrix() to account for batch variables correctly
1.5.1 Compare against UMAP
refactor_k8_age18 <- fread(refactor_k8_age18_dir)
colnames(refactor_k8_age18)[1] <- "IID"
eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf <- join(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, refactor_k8_age18, by = "IID", type = "inner")
eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf.tmp <- eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf[,c("IID", "UMAP1", "UMAP2", "rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8")]
eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf.long <- melt(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf.tmp, id.vars = c("IID", "UMAP1", "UMAP2"), measure.vars = c("rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8"), variable.name = "celltype", value.name = "CTP")
ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf.long, aes(x = UMAP1, y = UMAP2, alpha = 0.8)) + geom_point(aes(colour = CTP)) + facet_wrap(~ celltype) + scale_color_viridis(discrete = FALSE, option = "D")
rc1.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc1)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc2.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc2)) + scale_color_viridis(discrete = FALSE, option = "D") + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.rc3.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc3)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc4.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc4)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc5.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc5)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc6.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc6)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc7.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc7)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc8.umap <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc8)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
ggarrange(rc1.umap, rc2.umap, rc3.umap, rc4.umap, rc5.umap, rc6.umap, rc7.umap, rc8.umap, nrow = 3, ncol = 3)
1.5.2 Control clusters: check correlation from clusters based on all vs controls only
- correlation looks pretty good, though rc4, rc6, rc8 don’t do so well
refactor_k8_age18ctrl <- fread(refactor_k8_age18ctrl_dir)
colnames(refactor_k8_age18ctrl)[1] <- "IID"
refactor_k8_age18ctrl.tmp <- refactor_k8_age18ctrl
colnames(refactor_k8_age18ctrl.tmp) <- paste(colnames(refactor_k8_age18ctrl.tmp), "_ctrl", sep = "")
# Check correlation
datatable(data.frame(cor(refactor_k8_age18[,2:ncol(refactor_k8_age18)], refactor_k8_age18ctrl.tmp[,2:ncol(refactor_k8_age18ctrl.tmp)])) %>% mutate_if(is.numeric, ~round(., 2)))eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf <- join(eigenvec_sd02_age18_pheno_umap_ctp_jaffe, refactor_k8_age18ctrl, by = "IID", type = "inner")1.5.3 Compare against covariates: clusters_all
- These should have been regressed out by ReFACTor
- Exclude slide here to avoid +++ output - the plate should give a superficial idea
- Takeaway: there remain some significant effects of covariates (ie. age, sex, race, plate) on the ReFACTor components
summary(lm(rc1 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc1 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.196 -12.934 -1.427 9.980 75.203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.74859 5.16533 -0.726 0.46844
## groupSchizo -5.71956 2.17281 -2.632 0.00881 **
## age 0.20699 0.06826 3.032 0.00259 **
## sexM 1.47732 2.09572 0.705 0.48127
## raceCAUC -2.22015 1.95919 -1.133 0.25782
## plateLieber_244 -1.26248 3.91966 -0.322 0.74756
## plateLieber_289 -3.74736 3.96198 -0.946 0.34481
## plateLieber_30 4.69502 5.56413 0.844 0.39929
## plateLieber_315 -12.99816 4.30241 -3.021 0.00268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.17 on 394 degrees of freedom
## Multiple R-squared: 0.08608, Adjusted R-squared: 0.06753
## F-statistic: 4.639 on 8 and 394 DF, p-value: 1.965e-05summary(lm(rc2 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc2 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.6805 -2.8364 -1.0912 0.9004 23.4417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.87368 1.75476 -1.638 0.102
## groupSchizo 0.23613 0.73814 0.320 0.749
## age 0.01259 0.02319 0.543 0.587
## sexM 0.14543 0.71195 0.204 0.838
## raceCAUC 0.08739 0.66557 0.131 0.896
## plateLieber_244 1.35448 1.33158 1.017 0.310
## plateLieber_289 7.03617 1.34596 5.228 2.79e-07 ***
## plateLieber_30 -1.64481 1.89024 -0.870 0.385
## plateLieber_315 -2.39548 1.46161 -1.639 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.513 on 394 degrees of freedom
## Multiple R-squared: 0.2156, Adjusted R-squared: 0.1997
## F-statistic: 13.54 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc3 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc3 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.4890 -1.9929 -0.2243 1.2562 26.0247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.12607 0.88544 1.272 0.204208
## groupSchizo 1.04925 0.37246 2.817 0.005090 **
## age -0.06812 0.01170 -5.822 1.21e-08 ***
## sexM -0.39733 0.35925 -1.106 0.269398
## raceCAUC -0.31879 0.33584 -0.949 0.343088
## plateLieber_244 2.38348 0.67191 3.547 0.000436 ***
## plateLieber_289 1.77061 0.67916 2.607 0.009479 **
## plateLieber_30 1.66103 0.95380 1.741 0.082379 .
## plateLieber_315 2.62693 0.73752 3.562 0.000413 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.286 on 394 degrees of freedom
## Multiple R-squared: 0.1542, Adjusted R-squared: 0.137
## F-statistic: 8.978 on 8 and 394 DF, p-value: 2.463e-11summary(lm(rc4 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc4 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1055 -1.2898 -0.1654 1.1022 8.8620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.622879 0.563880 -6.425 3.81e-10 ***
## groupSchizo 1.380377 0.237198 5.820 1.22e-08 ***
## age 0.023766 0.007451 3.189 0.001539 **
## sexM 0.530209 0.228781 2.318 0.020986 *
## raceCAUC 0.352271 0.213878 1.647 0.100342
## plateLieber_244 0.896808 0.427895 2.096 0.036732 *
## plateLieber_289 1.546706 0.432514 3.576 0.000392 ***
## plateLieber_30 0.866805 0.607416 1.427 0.154361
## plateLieber_315 3.173083 0.469678 6.756 5.12e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.093 on 394 degrees of freedom
## Multiple R-squared: 0.2839, Adjusted R-squared: 0.2694
## F-statistic: 19.53 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc5 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc5 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0509 -0.9352 -0.0727 0.7095 10.9364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.817120 0.436784 1.871 0.062119 .
## groupSchizo -0.459487 0.183734 -2.501 0.012796 *
## age 0.004150 0.005772 0.719 0.472570
## sexM -0.338866 0.177215 -1.912 0.056579 .
## raceCAUC -0.029108 0.165671 -0.176 0.860620
## plateLieber_244 -1.512885 0.331449 -4.564 6.7e-06 ***
## plateLieber_289 -0.348133 0.335028 -1.039 0.299388
## plateLieber_30 -0.402215 0.470507 -0.855 0.393152
## plateLieber_315 1.387553 0.363815 3.814 0.000159 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.621 on 394 degrees of freedom
## Multiple R-squared: 0.3079, Adjusted R-squared: 0.2938
## F-statistic: 21.91 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc6 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc6 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3811 -0.6282 -0.0359 0.5441 9.6260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.777353 0.385530 2.016 0.044444 *
## groupSchizo -0.024106 0.162174 -0.149 0.881913
## age -0.029326 0.005095 -5.756 1.73e-08 ***
## sexM 0.339484 0.156420 2.170 0.030578 *
## raceCAUC 0.525841 0.146230 3.596 0.000364 ***
## plateLieber_244 -0.001722 0.292555 -0.006 0.995307
## plateLieber_289 0.625127 0.295714 2.114 0.035146 *
## plateLieber_30 1.052701 0.415295 2.535 0.011636 *
## plateLieber_315 -0.727861 0.321123 -2.267 0.023955 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.431 on 394 degrees of freedom
## Multiple R-squared: 0.2172, Adjusted R-squared: 0.2013
## F-statistic: 13.67 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc7 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc7 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1076 -0.4327 -0.0346 0.3352 10.9504
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.906101 0.257691 3.516 0.000489 ***
## groupSchizo 0.455125 0.108398 4.199 3.32e-05 ***
## age 0.002918 0.003405 0.857 0.392012
## sexM 0.083733 0.104552 0.801 0.423687
## raceCAUC -0.101293 0.097741 -1.036 0.300681
## plateLieber_244 -0.821094 0.195546 -4.199 3.32e-05 ***
## plateLieber_289 -1.424635 0.197657 -7.208 2.93e-12 ***
## plateLieber_30 -1.064588 0.277586 -3.835 0.000146 ***
## plateLieber_315 -2.967459 0.214641 -13.825 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9564 on 394 degrees of freedom
## Multiple R-squared: 0.431, Adjusted R-squared: 0.4194
## F-statistic: 37.3 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc8 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc8 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9294 -0.3620 0.0924 0.4551 8.0985
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.87607 0.24289 3.607 0.000350 ***
## groupSchizo -0.18630 0.10217 -1.823 0.068994 .
## age -0.02775 0.00321 -8.647 < 2e-16 ***
## sexM 0.22659 0.09855 2.299 0.022007 *
## raceCAUC -0.08381 0.09213 -0.910 0.363521
## plateLieber_244 0.07730 0.18431 0.419 0.675142
## plateLieber_289 0.94899 0.18630 5.094 5.45e-07 ***
## plateLieber_30 -0.12183 0.26164 -0.466 0.641740
## plateLieber_315 0.79356 0.20231 3.922 0.000103 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9015 on 394 degrees of freedom
## Multiple R-squared: 0.2963, Adjusted R-squared: 0.282
## F-statistic: 20.74 on 8 and 394 DF, p-value: < 2.2e-161.5.4 Compare against covariates: clusters_ctrl
- These should have been regressed out by ReFACTor
- Exclude slide here to avoid +++ output - the plate should give a superficial idea
- Takeaway: there remain some significant effects of covariates (ie. age, sex, race, plate) on the ReFACTor components
- Effects are slightly different (eg. sig assoc between rc8_ctrl and group but not rc8 calculated from all individuals)
summary(lm(rc1 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc1 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.505 -13.132 -1.012 10.475 72.888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.58577 5.14998 -0.890 0.37377
## groupSchizo -5.82787 2.16635 -2.690 0.00745 **
## age 0.21808 0.06805 3.204 0.00146 **
## sexM 1.70534 2.08948 0.816 0.41491
## raceCAUC -2.08817 1.95337 -1.069 0.28572
## plateLieber_244 -1.30844 3.90801 -0.335 0.73795
## plateLieber_289 -2.99201 3.95020 -0.757 0.44924
## plateLieber_30 4.66227 5.54759 0.840 0.40119
## plateLieber_315 -13.22719 4.28962 -3.084 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.11 on 394 degrees of freedom
## Multiple R-squared: 0.09027, Adjusted R-squared: 0.0718
## F-statistic: 4.887 on 8 and 394 DF, p-value: 9.085e-06summary(lm(rc2 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc2 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.3000 -3.0061 -0.9706 0.9739 21.9817
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.337812 1.768439 -1.322 0.1869
## groupSchizo 0.504245 0.743898 0.678 0.4983
## age -0.001956 0.023369 -0.084 0.9333
## sexM 0.151450 0.717503 0.211 0.8329
## raceCAUC 0.074550 0.670762 0.111 0.9116
## plateLieber_244 1.344387 1.341963 1.002 0.3171
## plateLieber_289 7.216097 1.356450 5.320 1.75e-07 ***
## plateLieber_30 -1.822313 1.904975 -0.957 0.3394
## plateLieber_315 -2.441416 1.473003 -1.657 0.0982 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.563 on 394 degrees of freedom
## Multiple R-squared: 0.22, Adjusted R-squared: 0.2042
## F-statistic: 13.89 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc3 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc3 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.837 -1.591 -0.317 1.164 32.219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.26426 0.80207 -0.329 0.741970
## groupSchizo 1.47522 0.33739 4.372 1.57e-05 ***
## age -0.04086 0.01060 -3.856 0.000135 ***
## sexM -0.17047 0.32542 -0.524 0.600682
## raceCAUC -0.18102 0.30422 -0.595 0.552164
## plateLieber_244 1.76320 0.60864 2.897 0.003979 **
## plateLieber_289 1.40724 0.61521 2.287 0.022702 *
## plateLieber_30 0.95659 0.86400 1.107 0.268895
## plateLieber_315 3.32407 0.66808 4.976 9.74e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.977 on 394 degrees of freedom
## Multiple R-squared: 0.1562, Adjusted R-squared: 0.139
## F-statistic: 9.115 on 8 and 394 DF, p-value: 1.613e-11summary(lm(rc4 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc4 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5734 -1.0505 -0.0138 0.7280 18.6318
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.110301 0.656045 -3.217 0.001404 **
## groupSchizo 0.451427 0.275967 1.636 0.102680
## age 0.009533 0.008669 1.100 0.272160
## sexM 0.618564 0.266175 2.324 0.020639 *
## raceCAUC 0.687164 0.248835 2.762 0.006022 **
## plateLieber_244 -0.141407 0.497833 -0.284 0.776525
## plateLieber_289 1.446438 0.503208 2.874 0.004267 **
## plateLieber_30 1.431998 0.706696 2.026 0.043404 *
## plateLieber_315 1.840211 0.546446 3.368 0.000833 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.435 on 394 degrees of freedom
## Multiple R-squared: 0.1653, Adjusted R-squared: 0.1484
## F-statistic: 9.755 on 8 and 394 DF, p-value: 2.219e-12summary(lm(rc5 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc5 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7165 -0.9833 0.0471 0.7964 16.8847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.91337 0.44422 2.056 0.04043 *
## groupSchizo -0.63766 0.18686 -3.412 0.00071 ***
## age 0.01635 0.00587 2.785 0.00561 **
## sexM -0.40719 0.18023 -2.259 0.02441 *
## raceCAUC -0.27224 0.16849 -1.616 0.10695
## plateLieber_244 -1.57844 0.33709 -4.683 3.9e-06 ***
## plateLieber_289 -1.02090 0.34073 -2.996 0.00291 **
## plateLieber_30 -1.27914 0.47851 -2.673 0.00783 **
## plateLieber_315 0.36993 0.37000 1.000 0.31802
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.649 on 394 degrees of freedom
## Multiple R-squared: 0.2454, Adjusted R-squared: 0.23
## F-statistic: 16.01 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc6 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc6 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1321 -0.9396 -0.0846 0.7006 8.5491
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.152023 0.417593 5.153 4.05e-07 ***
## groupSchizo -0.747526 0.175661 -4.256 2.61e-05 ***
## age -0.034995 0.005518 -6.342 6.24e-10 ***
## sexM -0.242724 0.169429 -1.433 0.15276
## raceCAUC 0.108214 0.158391 0.683 0.49488
## plateLieber_244 0.114497 0.316886 0.361 0.71806
## plateLieber_289 -0.002388 0.320307 -0.007 0.99406
## plateLieber_30 0.582538 0.449833 1.295 0.19608
## plateLieber_315 -0.928223 0.347829 -2.669 0.00793 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.55 on 394 degrees of freedom
## Multiple R-squared: 0.2133, Adjusted R-squared: 0.1973
## F-statistic: 13.35 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc7 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc7 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4728 -0.4083 0.0593 0.4395 10.7724
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.391486 0.288899 1.355 0.1762
## groupSchizo 0.281259 0.121526 2.314 0.0212 *
## age 0.003127 0.003818 0.819 0.4132
## sexM 0.257547 0.117214 2.197 0.0286 *
## raceCAUC -0.061622 0.109578 -0.562 0.5742
## plateLieber_244 -0.215077 0.219228 -0.981 0.3272
## plateLieber_289 -0.982224 0.221595 -4.433 1.21e-05 ***
## plateLieber_30 -0.774703 0.311204 -2.489 0.0132 *
## plateLieber_315 -2.634870 0.240636 -10.950 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.072 on 394 degrees of freedom
## Multiple R-squared: 0.3952, Adjusted R-squared: 0.3829
## F-statistic: 32.18 on 8 and 394 DF, p-value: < 2.2e-16summary(lm(rc8 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc8 ~ group + age + sex + race + plate, data = eigenvec_sd02_age18ctrl_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7388 -0.4152 0.0534 0.5095 5.7071
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.619663 0.240975 -2.571 0.0105 *
## groupSchizo -0.410174 0.101367 -4.046 6.26e-05 ***
## age -0.007010 0.003184 -2.201 0.0283 *
## sexM 0.102755 0.097770 1.051 0.2939
## raceCAUC -0.163560 0.091401 -1.789 0.0743 .
## plateLieber_244 0.911337 0.182862 4.984 9.36e-07 ***
## plateLieber_289 1.529894 0.184836 8.277 1.98e-15 ***
## plateLieber_30 0.583664 0.259580 2.248 0.0251 *
## plateLieber_315 2.007873 0.200718 10.003 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8944 on 394 degrees of freedom
## Multiple R-squared: 0.3048, Adjusted R-squared: 0.2907
## F-statistic: 21.59 on 8 and 394 DF, p-value: < 2.2e-161.5.5 Correlate with Jaffe processed cell-types: clusters_all
- do see strong correlation (r > 0.90) for rc1 and NeuN+/-
- correlation between rc2 and comp_es (0.61) / comp_npc (r = 0.68)
- interestingly though, not with age
1.5.6 Correlate with Jaffe processed cell-types: clusters_ctrl
- do see strong correlation (r > 0.90) for rc1 and NeuN+/-
- correlation between rc2 and comp_es (0.61) / comp_npc (r = 0.62)
- interestingly though, not with age
- rc3: stronger correlation with comp_neun_neg
- rc4: stronger correlation with comp_neun_neg
- rc5: weaker correlation with comp_neun_neg
- rc6: stronger correlation with comp_da_neuron
- rc7: weaker correlation with comp_es than before
- rc8: weaker correlation with comp_da_neuron
1.5.6.1 Follow up rc2
- Rationale: looks like there are 2 distinct clusters for rc2
- Wonder if this is age-related (there are some clumps here too)
- Plot rc2 vs npcs and overlay various variables
- Takeaway: looks batchy!
rc2_npc.group <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = group)) + geom_point(alpha = 0.8) + theme(legend.position = "bottom")
rc2_npc.sex <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = sex)) + geom_point(alpha = 0.8) + theme(legend.position = "bottom")
rc2_npc.age <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = age)) + geom_point(alpha = 0.8) + scale_colour_viridis() + theme(legend.position = "bottom")
rc2_npc.race <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = race)) + geom_point(alpha = 0.8) + theme(legend.position = "bottom")
rc2_npc.plate <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = plate)) + geom_point(alpha = 0.8) + scale_colour_viridis(discrete = TRUE) + theme(legend.position = "bottom")
ggarrange(rc2_npc.group, rc2_npc.sex, rc2_npc.age, rc2_npc.race, rc2_npc.plate, nrow = 3, ncol = 2)
Check Slide variable too
- on its own as the legend is big and messay
rc2_npc.slide <- ggplot(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, aes(x = rc2, y = comp_npc, colour = slide)) + geom_point(alpha = 0.8, aes(colour = as.factor(slide))) + scale_colour_viridis(discrete = TRUE) + theme(legend.position = "bottom")
rc2_npc.slide
1.5.7 Check probe annotations: clusters_all
- ReFACTor provides list of most informative probes, though doesn’t assign to celltypes
- Check annotations, simply based on 450K manifest
- Does look “neural”, but not canonical marker genes to my naive eye
1.5.8 Check probe annotations: clusters_ctrl
- looks very similar to above from a cursory look
1.5.9 Trace back top probes to ReFACTor components: clusters_all
Extract n=500 probes from OSCA to get a smaller matrix
- I think this number is more appropriate as n=500 is the default number of probes to include in the ReFACTor analysis
- Plot heatmap
- Output interactive table to identify probes relating most to each component
#refactor_k8_age18 <- fread(refactor_k8_age18_dir)
refactor_k8_age18_n500.ma <- fread(refactor_k8_age18_n500.ma_dir)
identical(refactor_k8_age18$IID, colnames(refactor_k8_age18_n500.ma)[2:ncol(refactor_k8_age18_n500.ma)])## [1] TRUE# TRUE
tmp.rc <- as.matrix(refactor_k8_age18[,c("rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8")])
tmp.beta <- as.matrix(refactor_k8_age18_n500.ma[,2:ncol(refactor_k8_age18_n500.ma)])
refactor_k8_age18_n500.cor <- cor(t(tmp.beta), tmp.rc)
rownames(refactor_k8_age18_n500.cor) <- refactor_k8_age18_n500.ma$ID
heatmap(refactor_k8_age18_n500.cor, scale="column", col = terrain.colors(120))
refactor_k8_age18_n500.cor.anno <- join(data.frame(probe = rownames(refactor_k8_age18_n500.cor), refactor_k8_age18_n500.cor), refactor_k8_age18_probe_anno.df, by = "probe", type = "inner")
datatable(refactor_k8_age18_n500.cor.anno %>% mutate_if(is.numeric, ~round(., 2)))1.5.10 Trace back top probes to ReFACTor components: clusters_ctrl
Extract n=500 probes from OSCA to get a smaller matrix
- I think this number is more appropriate as n=500 is the default number of probes to include in the ReFACTor analysis
- Plot heatmap
- Output interactive table to identify probes relating most to each component
#refactor_k8_age18 <- fread(refactor_k8_age18_dir)
refactor_k8_age18ctrl_n500.ma <- fread(refactor_k8_age18ctrl_n500.ma_dir)
identical(refactor_k8_age18ctrl$IID, colnames(refactor_k8_age18ctrl_n500.ma)[2:ncol(refactor_k8_age18ctrl_n500.ma)])## [1] TRUE# TRUE
tmp.rc <- as.matrix(refactor_k8_age18ctrl[,c("rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8")])
tmp.beta <- as.matrix(refactor_k8_age18ctrl_n500.ma[,2:ncol(refactor_k8_age18ctrl_n500.ma)])
refactor_k8_age18ctrl_n500.cor <- cor(t(tmp.beta), tmp.rc)
rownames(refactor_k8_age18ctrl_n500.cor) <- refactor_k8_age18ctrl_n500.ma$ID
heatmap(refactor_k8_age18ctrl_n500.cor, scale="column", col = terrain.colors(120))
refactor_k8_age18ctrl_n500.cor.anno <- join(data.frame(probe = rownames(refactor_k8_age18ctrl_n500.cor), refactor_k8_age18ctrl_n500.cor), refactor_k8_age18ctrl_probe_anno.df, by = "probe", type = "inner")
datatable(refactor_k8_age18ctrl_n500.cor.anno %>% mutate_if(is.numeric, ~round(., 2)))1.6 Unsupervised cell clusters (ReFACTor in GLINT): fetal
Settings for ReFACTor
- include as covariates: age, sex, race, plate
- needed to use model.matrix() to account for batch variables correctly
1.6.1 Compare against UMAP
refactor_k8_fetal <- fread(refactor_k8_fetal_dir)
colnames(refactor_k8_fetal)[1] <- "IID"
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf <- join(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe, refactor_k8_fetal, by = "IID", type = "inner")
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf.tmp <- eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf[,c("IID", "UMAP1", "UMAP2", "rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8")]
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf.long <- melt(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf.tmp, id.vars = c("IID", "UMAP1", "UMAP2"), measure.vars = c("rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8"), variable.name = "celltype", value.name = "CTP")
ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf.long, aes(x = UMAP1, y = UMAP2, alpha = 0.8)) + geom_point(aes(colour = CTP)) + facet_wrap(~ celltype) + scale_color_viridis(discrete = FALSE, option = "D")
rc1.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc1)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc2.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc2)) + scale_color_viridis(discrete = FALSE, option = "D") + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.rc3.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc3)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc4.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc4)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc5.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc5)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc6.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc6)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc7.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc7)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
rc8.umap <- ggplot(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf, aes(x = UMAP1, y = UMAP2)) + geom_point(alpha = 0.8, aes(colour = rc8)) + scale_color_viridis(discrete = FALSE, option = "D") + theme(legend.position = "bottom")
ggarrange(rc1.umap, rc2.umap, rc3.umap, rc4.umap, rc5.umap, rc6.umap, rc7.umap, rc8.umap, nrow = 3, ncol = 3)
1.6.2 Compare against covariates
- These should have been regressed out by ReFACTor
- Exclude slide here to avoid +++ output - the plate should give a superficial idea
- Don’t include group here as all controls in fetal group
summary(lm(rc1 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc1 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.677 -5.804 -2.817 1.703 117.368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.714 60.139 0.029 0.977
## age -10.547 143.085 -0.074 0.942
## sexM -7.152 8.271 -0.865 0.395
## raceCAUC -6.596 14.964 -0.441 0.663
## plateLieber_244 -6.519 12.948 -0.503 0.619
## plateLieber_289 -6.023 10.051 -0.599 0.554
## plateLieber_315 -1.845 24.657 -0.075 0.941
##
## Residual standard error: 23.39 on 28 degrees of freedom
## Multiple R-squared: 0.04766, Adjusted R-squared: -0.1564
## F-statistic: 0.2335 on 6 and 28 DF, p-value: 0.962summary(lm(rc2 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc2 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.106 -3.001 -1.155 1.483 18.661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2061 12.6291 -0.016 0.987
## age -2.1863 30.0475 -0.073 0.943
## sexM -0.4850 1.7370 -0.279 0.782
## raceCAUC -2.4158 3.1424 -0.769 0.448
## plateLieber_244 -2.8180 2.7191 -1.036 0.309
## plateLieber_289 -0.5870 2.1107 -0.278 0.783
## plateLieber_315 6.3660 5.1779 1.229 0.229
##
## Residual standard error: 4.911 on 28 degrees of freedom
## Multiple R-squared: 0.111, Adjusted R-squared: -0.07948
## F-statistic: 0.5828 on 6 and 28 DF, p-value: 0.7409summary(lm(rc3 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc3 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9885 -0.9276 0.1730 0.7611 2.4303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -24.6726 3.1111 -7.930 1.23e-08 ***
## age -57.5353 7.4020 -7.773 1.82e-08 ***
## sexM -0.3343 0.4279 -0.781 0.441266
## raceCAUC 0.8514 0.7741 1.100 0.280740
## plateLieber_244 2.6396 0.6698 3.941 0.000493 ***
## plateLieber_289 0.1540 0.5200 0.296 0.769208
## plateLieber_315 1.4494 1.2755 1.136 0.265452
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.21 on 28 degrees of freedom
## Multiple R-squared: 0.7382, Adjusted R-squared: 0.682
## F-statistic: 13.16 on 6 and 28 DF, p-value: 4.883e-07summary(lm(rc4 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc4 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7533 -0.6797 0.0000 0.6994 2.3721
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.7693 2.8118 1.341 0.19085
## age 12.0370 6.6899 1.799 0.08276 .
## sexM 0.4514 0.3867 1.167 0.25296
## raceCAUC -0.4589 0.6996 -0.656 0.51727
## plateLieber_244 1.9091 0.6054 3.154 0.00383 **
## plateLieber_289 3.7647 0.4699 8.011 1.01e-08 ***
## plateLieber_315 -0.8468 1.1528 -0.735 0.46871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.093 on 28 degrees of freedom
## Multiple R-squared: 0.7227, Adjusted R-squared: 0.6633
## F-statistic: 12.16 on 6 and 28 DF, p-value: 1.05e-06summary(lm(rc5 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc5 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4616 -0.4362 0.1402 0.4582 3.3423
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.2644 3.0651 -2.696 0.01173 *
## age -20.5002 7.2925 -2.811 0.00891 **
## sexM -0.4560 0.4216 -1.082 0.28866
## raceCAUC 0.2949 0.7627 0.387 0.70192
## plateLieber_244 -1.3699 0.6599 -2.076 0.04720 *
## plateLieber_289 -0.4027 0.5123 -0.786 0.43837
## plateLieber_315 0.8608 1.2567 0.685 0.49898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.192 on 28 degrees of freedom
## Multiple R-squared: 0.3342, Adjusted R-squared: 0.1916
## F-statistic: 2.343 on 6 and 28 DF, p-value: 0.0585summary(lm(rc6 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc6 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5380 -0.7423 0.0000 0.5810 1.6944
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.73744 2.52711 -1.479 0.15032
## age -9.35096 6.01257 -1.555 0.13112
## sexM 0.14143 0.34757 0.407 0.68716
## raceCAUC -0.03202 0.62880 -0.051 0.95974
## plateLieber_244 0.72617 0.54410 1.335 0.19275
## plateLieber_289 -1.20513 0.42235 -2.853 0.00805 **
## plateLieber_315 -2.03938 1.03611 -1.968 0.05901 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9827 on 28 degrees of freedom
## Multiple R-squared: 0.3784, Adjusted R-squared: 0.2452
## F-statistic: 2.841 on 6 and 28 DF, p-value: 0.02744summary(lm(rc7 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc7 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2178 -0.1937 -0.0293 0.2421 3.2523
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3269 2.2239 0.147 0.8842
## age 1.2865 5.2911 0.243 0.8097
## sexM -0.2369 0.3059 -0.774 0.4452
## raceCAUC 0.2260 0.5534 0.408 0.6861
## plateLieber_244 1.2503 0.4788 2.611 0.0143 *
## plateLieber_289 0.3276 0.3717 0.881 0.3857
## plateLieber_315 2.7928 0.9118 3.063 0.0048 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8648 on 28 degrees of freedom
## Multiple R-squared: 0.3528, Adjusted R-squared: 0.2141
## F-statistic: 2.544 on 6 and 28 DF, p-value: 0.04299summary(lm(rc8 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf))##
## Call:
## lm(formula = rc8 ~ age + sex + race + plate, data = eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.18498 -0.30955 -0.00028 0.27176 1.88630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.39160 2.12916 0.184 0.85540
## age 1.00925 5.06577 0.199 0.84352
## sexM -0.25843 0.29284 -0.882 0.38503
## raceCAUC 0.16994 0.52978 0.321 0.75077
## plateLieber_244 0.48351 0.45842 1.055 0.30056
## plateLieber_289 -0.02254 0.35584 -0.063 0.94995
## plateLieber_315 3.14950 0.87295 3.608 0.00119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8279 on 28 degrees of freedom
## Multiple R-squared: 0.3409, Adjusted R-squared: 0.1996
## F-statistic: 2.413 on 6 and 28 DF, p-value: 0.052511.6.3 Correlate with Jaffe processed cell-types
- good to see that there look to be trends between age and cellular composition as inferred by Jaffe et al.
- rc1 and increased comp_da_neuron / decreased comp_npc
- rc2 and increased NeuN -ive / decreased NeuN +ive
- rc3 and increased comp_es (+ decreased age which is a nice sanity check) and decreased NeuN +ive
- rc4 and decreased Neun -ive / increased NeuN +ive
- rc5 and increased comp_es
- rc6 and increased comp_da_neuron
- note that there is an outlier that may be skewing results
1.6.3.1 Identify and check rc1/comp_da_neuron outlier
- find this is the same person!
eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf[which(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf$rc1 == max(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf$rc1)),]## FID IID PC1 PC2 PC3 PC4
## 1: Sample19_Control Sample19_Control -0.674668 -0.580362 -0.340695 0.156399
## PC5 PC6 PC7 PC8 PC9 PC10 PC11
## 1: 0.0212083 0.0337158 -0.0202028 -0.0485313 -0.0969421 0.0292195 0.111271
## PC12 PC13 PC14 PC15 PC16 PC17 PC18
## 1: -0.0550832 -0.0284861 0.0184859 0.00581602 -0.0199274 0.00751685 0.00755818
## PC19 PC20 group age sex race plate slide
## 1: 0.0292292 -0.00518256 Control -0.421917 F AA Lieber_104 5815188023
## sentrix_row sentrix_col UMAP1 UMAP2 comp_da_neuron comp_es
## 1: 3 2 -0.4793697 0.7660486 0.1608904 0.1065557
## comp_neun_neg comp_neun_pos comp_npc rc1 rc2 rc3 rc4
## 1: 0.3388347 0.2658577 0 123.532 -2.596077 0.1559752 -0.9562762
## rc5 rc6 rc7 rc8
## 1: -0.1439078 -0.2354442 -0.02970016 -0.04059768eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf[which(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf$comp_da_neuron == max(eigenvec_sd02_fetal_pheno_umap_ctp_jaffe_rf$comp_da_neuron)),]## FID IID PC1 PC2 PC3 PC4
## 1: Sample19_Control Sample19_Control -0.674668 -0.580362 -0.340695 0.156399
## PC5 PC6 PC7 PC8 PC9 PC10 PC11
## 1: 0.0212083 0.0337158 -0.0202028 -0.0485313 -0.0969421 0.0292195 0.111271
## PC12 PC13 PC14 PC15 PC16 PC17 PC18
## 1: -0.0550832 -0.0284861 0.0184859 0.00581602 -0.0199274 0.00751685 0.00755818
## PC19 PC20 group age sex race plate slide
## 1: 0.0292292 -0.00518256 Control -0.421917 F AA Lieber_104 5815188023
## sentrix_row sentrix_col UMAP1 UMAP2 comp_da_neuron comp_es
## 1: 3 2 -0.4793697 0.7660486 0.1608904 0.1065557
## comp_neun_neg comp_neun_pos comp_npc rc1 rc2 rc3 rc4
## 1: 0.3388347 0.2658577 0 123.532 -2.596077 0.1559752 -0.9562762
## rc5 rc6 rc7 rc8
## 1: -0.1439078 -0.2354442 -0.02970016 -0.040597681.6.3.2 Re-run correlation removing this outlier
- note that there excluding just this individual induces a correlation between rc
- NO LONGER big asssociation: rc1 and increased comp_da_neuron / decreased comp_npc
- find there is still a nice correlation for some rc eg.
- rc2 and increased NeuN -ive / decreased NeuN +ive
- rc3 and increased comp_es (+ decreased age which is a nice sanity check) and decreased NeuN +ive
- rc4 and decreased Neun -ive / increased NeuN +ive
- rc5 and increased comp_es
1.6.4 Check probe annotations
- ReFACTor provides list of most informative probes, though doesn’t assign to celltypes
- Check annotations, simply based on 450K manifest
- Don’t seem glaringly “neural” to my naive eye, seem to be more poorly annotated genes (eg. pseudogenes, miRNA, LINCs, etc.)
1.6.5 Trace back top probes to ReFACTor components
Extract n=500 probes from OSCA to get a smaller matrix
- I think this number is more appropriate as n=500 is the default number of probes to include in the ReFACTor analysis
- Plot heatmap
- Output interactive table to identify probes relating most to each component
#refactor_k8_fetal <- fread(refactor_k8_fetal_dir)
refactor_k8_fetal_n500.ma <- fread(refactor_k8_fetal_n500.ma_dir)
identical(refactor_k8_fetal$IID, colnames(refactor_k8_fetal_n500.ma)[2:ncol(refactor_k8_fetal_n500.ma)])## [1] TRUE# TRUE
tmp.rc <- as.matrix(refactor_k8_fetal[,c("rc1", "rc2", "rc3", "rc4", "rc5", "rc6", "rc7", "rc8")])
tmp.beta <- as.matrix(refactor_k8_fetal_n500.ma[,2:ncol(refactor_k8_fetal_n500.ma)])
refactor_k8_fetal_n500.cor <- cor(t(tmp.beta), tmp.rc)
rownames(refactor_k8_fetal_n500.cor) <- refactor_k8_fetal_n500.ma$ID
heatmap(refactor_k8_fetal_n500.cor, scale="column", col = terrain.colors(120))
refactor_k8_fetal_n500.cor.anno <- join(data.frame(probe = rownames(refactor_k8_fetal_n500.cor), refactor_k8_fetal_n500.cor), refactor_k8_fetal_probe_anno.df, by = "probe", type = "inner")
datatable(refactor_k8_fetal_n500.cor.anno %>% mutate_if(is.numeric, ~round(., 2)))1.7 Supervised (Houseman): age18
hm_na2 <- fread(hm_na2_dir)
hm_na5 <- fread(hm_na5_dir)
colnames(hm_na2)[1] <- "IID"
colnames(hm_na5)[1] <- "IID"1.7.1 Join datasets
eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf_hmna2 <- join(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, hm_na2, by = "IID", type = "inner")
eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf_hmna5 <- join(eigenvec_sd02_age18_pheno_umap_ctp_jaffe_rf, hm_na5, by = "IID", type = "inner")1.7.2 Correlations between supervised and unsupervised
100 nearest probes
500 nearest probes
